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500 |
Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing
|
[
"Jiayi Fu",
"Siyu Liu",
"Zikun Liu",
"Chun-Le Guo",
"Hyunhee Park",
"Ruiqi Wu",
"Guoqing Wang",
"Chongyi Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Iterative_Predictor-Critic_Code_Decoding_for_Real-World_Image_Dehazing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Iterative_Predictor-Critic_Code_Decoding_for_Real-World_Image_Dehazing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Iterative_Predictor-Critic_Code_CVPR_2025_supplemental.pdf
|
2503.13147
|
@InProceedings{Fu_2025_CVPR,
author = {Fu, Jiayi and Liu, Siyu and Liu, Zikun and Guo, Chun-Le and Park, Hyunhee and Wu, Ruiqi and Wang, Guoqing and Li, Chongyi},
title = {Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12700-12709}
}
|
We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance. Our idea stems from the observations that 1) the degradation of hazy images varies with haze density and scene depth, and 2) clear regions play crucial cues in restoring dense haze regions. However, it is nontrivial to progressively refine the obtained codes in subsequent iterations, owing to the difficulty in determining which codes should be retained or replaced at each iteration. Another key insight of our study is to propose Code-Critic to capture interrelations among codes. The Code-Critic is used to evaluate code correlations and then resample a set of codes with the highest mask scores, i.e., a higher score indicates that the code is more likely to be rejected, which helps retain more accurate codes and predict difficult ones. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods in real-world dehazing. Our code will be made publicly available.
|
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] |
501 |
RNG: Relightable Neural Gaussians
|
[
"Jiahui Fan",
"Fujun Luan",
"Jian Yang",
"Milos Hasan",
"Beibei Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_RNG_Relightable_Neural_Gaussians_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_RNG_Relightable_Neural_Gaussians_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_RNG_Relightable_Neural_CVPR_2025_supplemental.pdf
|
2409.19702
|
@InProceedings{Fan_2025_CVPR,
author = {Fan, Jiahui and Luan, Fujun and Yang, Jian and Hasan, Milos and Wang, Beibei},
title = {RNG: Relightable Neural Gaussians},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26525-26534}
}
|
3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especially for objects with ill-defined shapes (fur, fabric, etc.), remains a challenging task. The decomposition between light, geometry, and material is ambiguous, especially if either smooth surface assumptions or surface-based analytical shading models do not apply. We propose Relightable Neural Gaussians (RNG), a novel 3DGS-based framework that enables the relighting of objects with both hard surfaces or soft boundaries, while avoiding assumptions on the shading model. We condition the radiance at each point on both view and light directions. We also introduce a shadow cue, as well as a depth refinement network to improve shadow accuracy. Finally, we propose a hybrid forward-deferred fitting strategy to balance geometry and appearance quality. Our method achieves significantly faster training (1.3 hours) and rendering (60 frames per second) compared to a prior method based on neural radiance fields and produces higher-quality shadows than a concurrent 3DGS-based method.
|
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] |
502 |
Towards Realistic Example-based Modeling via 3D Gaussian Stitching
|
[
"Xinyu Gao",
"Ziyi Yang",
"Bingchen Gong",
"Xiaoguang Han",
"Sipeng Yang",
"Xiaogang Jin"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Towards_Realistic_Example-based_Modeling_via_3D_Gaussian_Stitching_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Towards_Realistic_Example-based_Modeling_via_3D_Gaussian_Stitching_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Towards_Realistic_Example-based_CVPR_2025_supplemental.zip
|
2408.15708
|
@InProceedings{Gao_2025_CVPR,
author = {Gao, Xinyu and Yang, Ziyi and Gong, Bingchen and Han, Xiaoguang and Yang, Sipeng and Jin, Xiaogang},
title = {Towards Realistic Example-based Modeling via 3D Gaussian Stitching},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26597-26607}
}
|
Using parts of existing models to rebuild new models, commonly termed as example-based modeling, is a classical methodology in the realm of computer graphics. Previous works mostly focus on shape composition, making them very hard to use for realistic composition of 3D objects captured from real-world scenes. This leads to combining multiple NeRFs into a single 3D scene to achieve seamless appearance blending. However, the current SeamlessNeRF method struggles to achieve interactive editing and harmonious stitching for real-world scenes due to its gradient-based strategy and grid-based representation. To this end, we present an example-based modeling method that combines multiple Gaussian fields in a point-based representation using sample-guided synthesis. Specifically, as for composition, we create a GUI to segment and transform multiple fields in real time, easily obtaining a semantically meaningful composition of models represented by 3D Gaussian Splatting (3DGS). For texture blending, due to the discrete and irregular nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF is not supported. Thus, a novel sampling-based cloning method is proposed to harmonize the blending while preserving the original rich texture and content. Our workflow consists of three steps: 1) real-time segmentation and transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis to identify boundary points in the intersecting area between the source and target models, and 3) two-phase optimization of the target model using sampling-based cloning and gradient constraints. Extensive experimental results validate that our approach significantly outperforms previous works in terms of realistic synthesis, demonstrating its practicality.
|
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] |
503 |
Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning
|
[
"Bardia Safaei",
"Faizan Siddiqui",
"Jiacong Xu",
"Vishal M. Patel",
"Shao-Yuan Lo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Safaei_Filter_Images_First_Generate_Instructions_Later_Pre-Instruction_Data_Selection_for_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Safaei_Filter_Images_First_Generate_Instructions_Later_Pre-Instruction_Data_Selection_for_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Safaei_Filter_Images_First_CVPR_2025_supplemental.pdf
|
2503.07591
|
@InProceedings{Safaei_2025_CVPR,
author = {Safaei, Bardia and Siddiqui, Faizan and Xu, Jiacong and Patel, Vishal M. and Lo, Shao-Yuan},
title = {Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14247-14256}
}
|
Visual instruction tuning (VIT) for large vision-language models (LVLMs) requires training on expansive datasets of image-instruction pairs, which can be costly. Recent efforts in VIT data selection aim to select a small subset of high-quality image-instruction pairs, reducing VIT runtime while maintaining performance comparable to full-scale training. However, a major challenge often overlooked is that generating instructions from unlabeled images for VIT is highly expensive. Most existing VIT datasets rely heavily on human annotations or paid services like the GPT API, which limits users with constrained resources from creating VIT datasets for custom applications. To address this, we introduce Pre-Instruction Data Selection (PreSel), a more practical data selection paradigm that directly selects the most beneficial unlabeled images and generates instructions only for the selected images. PreSel first estimates the relative importance of each vision task within VIT datasets to derive task-wise sampling budgets. It then clusters image features within each task, selecting the most representative images with the budget. This approach reduces computational overhead for both instruction generation during VIT data formation and LVLM fine-tuning. By generating instructions for only 15% of the images, PreSel achieves performance comparable to full-data VIT on the LLaVA-1.5 and Vision-Flan datasets. The link to our project page: https://bardisafa.github.io/PreSel
|
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] |
504 |
Gradient-Guided Annealing for Domain Generalization
|
[
"Aristotelis Ballas",
"Christos Diou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ballas_Gradient-Guided_Annealing_for_Domain_Generalization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ballas_Gradient-Guided_Annealing_for_Domain_Generalization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ballas_Gradient-Guided_Annealing_for_CVPR_2025_supplemental.pdf
|
2502.20162
|
@InProceedings{Ballas_2025_CVPR,
author = {Ballas, Aristotelis and Diou, Christos},
title = {Gradient-Guided Annealing for Domain Generalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20558-20568}
}
|
Domain Generalization (DG) research has gained considerable traction as of late, since the ability to generalize to unseen data distributions is a requirement that eludes even state-of-the-art training algorithms. In this paper we observe that the initial iterations of model training play a key role in domain generalization effectiveness, since the loss landscape may be significantly different across the training and test distributions, contrary to the case of i.i.d. data. Conflicts between gradients of the loss components of each domain lead the optimization procedure to undesirable local minima that do not capture the domain-invariant features of the target classes. We propose alleviating domain conflicts in model optimization, by iteratively annealing the parameters of a model in the early stages of training and searching for points where gradients align between domains. By discovering a set of parameter values where gradients are updated towards the same direction for each data distribution present in the training set, the proposed Gradient-Guided Annealing (GGA) algorithm encourages models to seek out minima that exhibit improved robustness against domain shifts. The efficacy of GGA is evaluated on five widely accepted and challenging image classification domain generalization benchmarks, where its use alone is able to establish highly competitive or even state-of-the-art performance. Moreover, when combined with previously proposed domain-generalization algorithms it is able to consistently improve their effectiveness by significant margins.
|
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] |
505 |
Generative Sparse-View Gaussian Splatting
|
[
"Hanyang Kong",
"Xingyi Yang",
"Xinchao Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_Generative_Sparse-View_Gaussian_Splatting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_Generative_Sparse-View_Gaussian_Splatting_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_Generative_Sparse-View_Gaussian_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Kong_2025_CVPR,
author = {Kong, Hanyang and Yang, Xingyi and Wang, Xinchao},
title = {Generative Sparse-View Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26745-26755}
}
|
Novel view synthesis from limited observations remains a significant challenge due to the lack of information in under-sampled regions, often resulting in noticeable artifacts. We introduce Generative Sparse-view Gaussian Splatting (GS-GS), a general pipeline designed to enhance the rendering quality of 3D/4D Gaussian Splatting (GS) when training views are sparse. Our method generates unseen views using generative models, specifically leveraging pre-trained image diffusion models to iteratively refine view consistency and hallucinate additional images at pseudo views. This approach improves 3D/4D scene reconstruction by explicitly enforcing semantic correspondences during the generation of unseen views, thereby enhancing geometric consistency--unlike purely generative methods that often fail to maintain view consistency. Extensive evaluations on various 3D/4D datasets--including Blender, LLFF, Mip-NeRF360, and Neural 3D Video--demonstrate that our GS-GS outperforms existing state-of-the-art methods in rendering quality without sacrificing efficiency.
|
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506 |
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
|
[
"James Burgess",
"Jeffrey J Nirschl",
"Laura Bravo-Sánchez",
"Alejandro Lozano",
"Sanket Rajan Gupte",
"Jesus G. Galaz-Montoya",
"Yuhui Zhang",
"Yuchang Su",
"Disha Bhowmik",
"Zachary Coman",
"Sarina M Hasan",
"Alexandra Johannesson",
"William D. Leineweber",
"Malvika G Nair",
"Ridhi Yarlagadda",
"Connor Zuraski",
"Wah Chiu",
"Sarah Cohen",
"Jan N. Hansen",
"Manuel D Leonetti",
"Chad Liu",
"Emma Lundberg",
"Serena Yeung-Levy"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Burgess_MicroVQA_A_Multimodal_Reasoning_Benchmark_for_Microscopy-Based_Scientific_Research_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Burgess_MicroVQA_A_Multimodal_Reasoning_Benchmark_for_Microscopy-Based_Scientific_Research_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Burgess_MicroVQA_A_Multimodal_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Burgess_2025_CVPR,
author = {Burgess, James and Nirschl, Jeffrey J and Bravo-S\'anchez, Laura and Lozano, Alejandro and Gupte, Sanket Rajan and Galaz-Montoya, Jesus G. and Zhang, Yuhui and Su, Yuchang and Bhowmik, Disha and Coman, Zachary and Hasan, Sarina M and Johannesson, Alexandra and Leineweber, William D. and Nair, Malvika G and Yarlagadda, Ridhi and Zuraski, Connor and Chiu, Wah and Cohen, Sarah and Hansen, Jan N. and Leonetti, Manuel D and Liu, Chad and Lundberg, Emma and Yeung-Levy, Serena},
title = {MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19552-19564}
}
|
Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based 'RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at https://huggingface.co/datasets/jmhb/microvqa and project at https://jmhb0.github.io/microvqa.
|
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] |
507 |
Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
|
[
"Tianyi Zhu",
"Dongwei Ren",
"Qilong Wang",
"Xiaohe Wu",
"Wangmeng Zuo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Generative_Inbetweening_through_Frame-wise_Conditions-Driven_Video_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Generative_Inbetweening_through_Frame-wise_Conditions-Driven_Video_Generation_CVPR_2025_paper.pdf
| null |
2412.11755
|
@InProceedings{Zhu_2025_CVPR,
author = {Zhu, Tianyi and Ren, Dongwei and Wang, Qilong and Wu, Xiaohe and Zuo, Wangmeng},
title = {Generative Inbetweening through Frame-wise Conditions-Driven Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27968-27978}
}
|
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, generative inbetweening still faces challenges in maintaining temporal stability due to the ambiguous interpolation path between two key frames. This issue becomes particularly severe when there is a large motion gap between input frames. In this paper, we propose a straightforward yet highly effective Frame-wise Conditions-driven Video Generation (FCVG) method that significantly enhances the temporal stability of interpolated video frames. Specifically, our FCVG provides an explicit condition for each frame, making it much easier to identify the interpolation path between two input frames and thus ensuring temporally stable production of visually plausible video frames. To achieve this, we suggest extracting matched lines from two input frames that can then be easily interpolated frame by frame, serving as frame-wise conditions seamlessly integrated into existing video generation models. In extensive evaluations covering diverse scenarios such as natural landscapes, complex human poses, camera movements and animations, existing methods often exhibit incoherent transitions across frames. In contrast, our FCVG demonstrates the capability to generate temporally stable videos using both linear and non-linear interpolation curves. Our project page and code are available at https://fcvg-inbetween.github.io/.
|
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] |
508 |
DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness
|
[
"Yiming Zhong",
"Qi Jiang",
"Jingyi Yu",
"Yuexin Ma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_DexGrasp_Anything_Towards_Universal_Robotic_Dexterous_Grasping_with_Physics_Awareness_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_DexGrasp_Anything_Towards_Universal_Robotic_Dexterous_Grasping_with_Physics_Awareness_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_DexGrasp_Anything_Towards_CVPR_2025_supplemental.zip
|
2503.08257
|
@InProceedings{Zhong_2025_CVPR,
author = {Zhong, Yiming and Jiang, Qi and Yu, Jingyi and Ma, Yuexin},
title = {DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22584-22594}
}
|
A dexterous hand capable of grasping any object is essential for the development of general-purpose embodied intelligent robots. However, due to the high degree of freedom in dexterous hands and the vast diversity of objects, generating high-quality, usable grasping poses in a robust manner is a significant challenge. In this paper, we introduce DexGrasp Anything, a method that effectively integrates physical constraints into both the training and sampling phases of a diffusion-based generative model, achieving state-of-the-art performance across nearly all open datasets. Additionally, we present a new dexterous grasping dataset containing over 3.4 million diverse grasping poses for more than 15k different objects, demonstrating its potential to advance universal dexterous grasping. The code of our method and our dataset will be publicly released soon.
|
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] |
509 |
CustAny: Customizing Anything from A Single Example
|
[
"Lingjie Kong",
"Kai Wu",
"Chengming Xu",
"Xiaobin Hu",
"Wenhui Han",
"Jinlong Peng",
"Donghao Luo",
"Mengtian Li",
"Jiangning Zhang",
"Chengjie Wang",
"Yanwei Fu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kong_CustAny_Customizing_Anything_from_A_Single_Example_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_CustAny_Customizing_Anything_from_A_Single_Example_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kong_CustAny_Customizing_Anything_CVPR_2025_supplemental.pdf
|
2406.11643
|
@InProceedings{Kong_2025_CVPR,
author = {Kong, Lingjie and Wu, Kai and Xu, Chengming and Hu, Xiaobin and Han, Wenhui and Peng, Jinlong and Luo, Donghao and Li, Mengtian and Zhang, Jiangning and Wang, Chengjie and Fu, Yanwei},
title = {CustAny: Customizing Anything from A Single Example},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20916-20925}
}
|
Recent advances in diffusion-based text-to-image models have simplified creating high-fidelity images, but preserving the identity (ID) of specific elements, like a personal dog, is still challenging.Object customization, using reference images and textual descriptions, is key to addressing this issue. Current object customization methods are either object-specific, requiring extensive fine-tuning, or object-agnostic, offering zero-shot customization but limited to specialized domains. The primary issue of promoting zero-shot object customization from specific domains to the general domain is to establish a large-scale general ID dataset for model pre-training, which is time-consuming and labor-intensive. In this paper, we propose a novel pipeline to construct a large dataset of general objects and build the Multi-Category ID-Consistent (MC-IDC) dataset, featuring 315k text-image samples across 10k categories. With the help of MC-IDC, we introduce Customizing Anything (CustAny), a zero-shot framework that maintains ID fidelity and supports flexible text editing for general objects. CustAny features three key components: a general ID extraction module, a dual-level ID injection module, and an ID-aware decoupling module, allowing it to customize any object from a single reference image and text prompt. Experiments demonstrate that CustAny outperforms existing methods in both general object customization and specialized domains like human customization and virtual try-on. Our contributions include a large-scale dataset, the CustAny framework and novel ID processing to advance this field. The official project page is in https://lingjiekong-fdu.github.io.
|
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510 |
Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks
|
[
"Haijin Zeng",
"Xiangming Wang",
"Yongyong Chen",
"Jingyong Su",
"Jie Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Vision-Language_Gradient_Descent-driven_All-in-One_Deep_Unfolding_Networks_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Vision-Language_Gradient_Descent-driven_All-in-One_Deep_Unfolding_Networks_CVPR_2025_paper.pdf
| null |
2503.16930
|
@InProceedings{Zeng_2025_CVPR,
author = {Zeng, Haijin and Wang, Xiangming and Chen, Yongyong and Su, Jingyong and Liu, Jie},
title = {Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7524-7533}
}
|
Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental conditions. Existing Deep Unfolding Networks (DUNs) offer stable restoration performance but require manual selection of degradation matrices for each degradation type, limiting their adaptability across diverse scenarios.To address this issue, we propose the Vision-Language-guided Unfolding Network (VLU-Net), a unified DUN framework for handling multiple degradation types simultaneously.VLU-Net leverages a Vision-Language Model (VLM) refined on degraded image-text pairs to align image features with degradation descriptions, selecting the appropriate transform for target degradation.By integrating an automatic VLM-based gradient estimation strategy into the Proximal Gradient Descent (PGD) algorithm, VLU-Net effectively tackles complex multi-degradation restoration tasks while maintaining interpretability. Furthermore, we design a hierarchical feature unfolding structure to enhance VLU-Net framework, efficiently synthesizing degradation patterns across various levels.VLU-Net is the first all-in-one DUN framework and outperforms current leading one-by-one and all-in-one end-to-end methods by 3.74 dB on the SOTS dehazing dataset and 1.70 dB on the Rain100L deraining dataset.
|
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] |
511 |
3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer
|
[
"Jiajun Deng",
"Tianyu He",
"Li Jiang",
"Tianyu Wang",
"Feras Dayoub",
"Ian Reid"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_3D-LLaVA_Towards_Generalist_3D_LMMs_with_Omni_Superpoint_Transformer_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_3D-LLaVA_Towards_Generalist_3D_LMMs_with_Omni_Superpoint_Transformer_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_3D-LLaVA_Towards_Generalist_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Deng_2025_CVPR,
author = {Deng, Jiajun and He, Tianyu and Jiang, Li and Wang, Tianyu and Dayoub, Feras and Reid, Ian},
title = {3D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformer},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3772-3782}
}
|
Current 3D Large Multimodal Models (3D LMMs) have shown tremendous potential in 3D-vision-based dialogue and reasoning. However, how to further enhance 3D LMMs to achieve fine-grained scene understanding and facilitate flexible human-agent interaction remains a challenging problem. In this work, we introduce 3D-LLaVA, a simple yet highly powerful 3D LMM designed to act as an intelligent assistant in comprehending, reasoning, and interacting with the 3D world. Unlike existing top-performing methods that rely on complicated pipelines--such as offline multi-view feature extraction or additional task-specific heads--3D-LLaVA adopts a minimalist design with integrated architecture and only takes point clouds as input. At the core of 3D-LLaVA is a new Omni Superpoint Transformer (OST), which integrates three functionalities: (1) a visual feature selector that converts and selects visual tokens, (2) a visual prompt encoder that embeds interactive visual prompts into the visual token space, and (3) a referring mask decoder that produces 3D masks based on text description. This versatile OST is empowered by the hybrid pretraining to obtain perception priors and leveraged as the visual connector that bridges the 3D data to the LLM. After performing unified instruction tuning, our 3D-LLaVA reports impressive results on various benchmarks. The code and model will be released at https://github.com/djiajunustc/3D-LLaVA.
|
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] |
512 |
Event-based Video Super-Resolution via State Space Models
|
[
"Zeyu Xiao",
"Xinchao Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Event-based_Video_Super-Resolution_via_State_Space_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Event-based_Video_Super-Resolution_via_State_Space_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Event-based_Video_Super-Resolution_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Xiao_2025_CVPR,
author = {Xiao, Zeyu and Wang, Xinchao},
title = {Event-based Video Super-Resolution via State Space Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12564-12574}
}
|
Exploiting temporal correlations is crucial for video super-resolution (VSR). Recent approaches enhance this by incorporating event cameras. In this paper, we introduce MamEVSR, a Mamba-based network for event-based VSR that leverages the selective state space model, Mamba. MamEVSR stands out by offering global receptive field coverage with linear computational complexity, thus addressing the limitations of convolutional neural networks and Transformers. The key components of MamEVSR include: (1) The interleaved Mamba (iMamba) block, which interleaves tokens from adjacent frames and applies multidirectional selective state space modeling, enabling efficient feature fusion and propagation across bi-directional frames while maintaining linear complexity. (2) The crossmodality Mamba (cMamba) block facilitates further interaction and aggregation between event information and the output from the iMamba block. The cMamba block can leverage complementary spatio-temporal information from both modalities and allows MamEVSR to capture finermotion details. Experimental results show that the proposed MamEVSR achieves superior performance on various datasets quantitatively and qualitatively.
|
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] |
513 |
PoseTraj: Pose-Aware Trajectory Control in Video Diffusion
|
[
"Longbin Ji",
"Lei Zhong",
"Pengfei Wei",
"Changjian Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_PoseTraj_Pose-Aware_Trajectory_Control_in_Video_Diffusion_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_PoseTraj_Pose-Aware_Trajectory_Control_in_Video_Diffusion_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_PoseTraj_Pose-Aware_Trajectory_CVPR_2025_supplemental.pdf
|
2503.16068
|
@InProceedings{Ji_2025_CVPR,
author = {Ji, Longbin and Zhong, Lei and Wei, Pengfei and Li, Changjian},
title = {PoseTraj: Pose-Aware Trajectory Control in Video Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22776-22785}
}
|
Recent advancements in trajectory-guided video generation have achieved notable progress. However, existing models still face challenges in generating object motions with potentially changing 6D poses under wide-range rotations, due to limited 3D understanding. To address this problem, we introduce PoseTraj, a pose-aware video dragging model for generating 3D-aligned motion from 2D trajectories. Our method adopts a novel two-stage pose-aware pretraining framework, improving 3D understanding across diverse trajectories. Specifically, we propose a large-scale synthetic dataset PoseTraj-10k, containing 10k videos of objects following rotational trajectories, and enhance the model perception of object pose changes by incorporating 3D bounding boxes as intermediate supervision signals. Following this, we fine-tune the trajectory-controlling module on real-world videos, applying an additional camera-disentanglement module to further refine motion accuracy. Experiments on various benchmark datasets demonstrate that our method not only excels in 3D pose-aligned dragging for rotational trajectories but also outperforms existing baselines in trajectory accuracy and video quality.
|
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] |
514 |
Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding
|
[
"Pedro Hermosilla",
"Christian Stippel",
"Leon Sick"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hermosilla_Masked_Scene_Modeling_Narrowing_the_Gap_Between_Supervised_and_Self-Supervised_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hermosilla_Masked_Scene_Modeling_Narrowing_the_Gap_Between_Supervised_and_Self-Supervised_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hermosilla_Masked_Scene_Modeling_CVPR_2025_supplemental.pdf
|
2504.06719
|
@InProceedings{Hermosilla_2025_CVPR,
author = {Hermosilla, Pedro and Stippel, Christian and Sick, Leon},
title = {Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14835-14844}
}
|
Self-supervised learning has transformed 2D computer vision by enabling models trained on large, unannotated datasets to provide versatile off-the-shelf features that perform similarly to models trained with labels. However, in 3D scene understanding, self-supervised methods are typically only used as a weight initialization step for task-specific fine-tuning, limiting their utility for general-purpose feature extraction. This paper aims to address this shortcoming by proposing a robust evaluation protocol specifically designed to assess the quality of self-supervised features for 3D scene understanding. Our protocol uses multi-resolution feature sampling of hierarchical models to create rich point-level representations that capture the semantic capabilities of the model and, hence, are suitable for evaluation with linear probing and nearest-neighbor methods. Furthermore, we introduce the first self-supervised model that performs similarly to supervised models when only off-the-shelf features are used in a linear probing setup. In particular, our model is trained natively in 3D with a novel self-supervised approach based on a Masked Scene Modeling objective, which reconstructs deep features of masked patches in a bottom-up manner and is specifically tailored to hierarchical 3D models. Our experiments not only demonstrate that our method achieves competitive performance to supervised models, but also surpasses existing self-supervised approaches by a large margin.
|
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] |
515 |
VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks
|
[
"Jinseong Jang",
"Chunfei Ma",
"Byeongwon Lee"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_VL2Lite_Task-Specific_Knowledge_Distillation_from_Large_Vision-Language_Models_to_Lightweight_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_VL2Lite_Task-Specific_Knowledge_Distillation_from_Large_Vision-Language_Models_to_Lightweight_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Jang_2025_CVPR,
author = {Jang, Jinseong and Ma, Chunfei and Lee, Byeongwon},
title = {VL2Lite: Task-Specific Knowledge Distillation from Large Vision-Language Models to Lightweight Networks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30073-30083}
}
|
Deploying high-performing neural networks in resource-constrained environments poses a significant challenge due to the computational demands of large-scale models. We introduce VL2Lite, a knowledge distillation framework designed to enhance the performance of lightweight neural networks in image classification tasks by leveraging the rich representational knowledge from Vision-Language Models (VLMs). VL2Lite directly integrates multi-modal knowledge from VLMs into compact models during training, effectively compensating for the limited computational and modeling capabilities of smaller networks. By transferring high-level features and complex data representations, our approach improves the accuracy and efficiency of image classification tasks without increasing computational overhead during inference. Experimental evaluations demonstrate that VL2Lite achieves up to a 7% improvement in classification performance across various datasets. This method addresses the challenge of deploying accurate models in environments with constrained computational resources, offering a balanced solution between model complexity and operational efficiency.
|
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] |
516 |
Boost the Inference with Co-training: A Depth-guided Mutual Learning Framework for Semi-supervised Medical Polyp Segmentation
|
[
"Yuxin Li",
"Zihao Zhu",
"Yuxiang Zhang",
"Yifan Chen",
"Zhibin Yu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Boost_the_Inference_with_Co-training_A_Depth-guided_Mutual_Learning_Framework_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Boost_the_Inference_with_Co-training_A_Depth-guided_Mutual_Learning_Framework_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Li_2025_CVPR,
author = {Li, Yuxin and Zhu, Zihao and Zhang, Yuxiang and Chen, Yifan and Yu, Zhibin},
title = {Boost the Inference with Co-training: A Depth-guided Mutual Learning Framework for Semi-supervised Medical Polyp Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10394-10403}
}
|
Semi-supervised polyp segmentation has made significant progress in recent years as a potential solution for computer-assisted treatment. Since depth images can provide extra information other than RGB images to help segment these problematic areas, depth-assisted polyp segmentation has gained much attention. However, the utilization of depth information is still worth studying. The existing RGB-D segmentation methods rely on depth data in the inference stage, limiting their clinical applications. To tackle this problem, we propose a semi-supervised polyp segmentation framework based on the mean teacher architecture. We establish an auxiliary student network with depth images as input in the training stage, and we propose a depth-guided cross-modal mutual learning strategy to promote the learning of complementary information between different student networks. Meanwhile, we use the high-confidence pseudo-labels generated by the auxiliary student network to guide the learning progress of the main student network from different perspectives. Our model does not need depth data in the inference phase. In addition, we introduce a depth-guided patch augmentation method to improve the model's learning performance in difficult regions of unlabeled polyp images. Experimental results show that our method achieves state-of-the-art performance under different label conditions on five polyp datasets. The code is available at https://github.com/pingchuan/RD-Net.
|
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] |
517 |
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding
|
[
"Chaoyu Li",
"Eun Woo Im",
"Pooyan Fazli"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VidHalluc_Evaluating_Temporal_Hallucinations_in_Multimodal_Large_Language_Models_for_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VidHalluc_Evaluating_Temporal_Hallucinations_in_Multimodal_Large_Language_Models_for_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_VidHalluc_Evaluating_Temporal_CVPR_2025_supplemental.pdf
|
2412.03735
|
@InProceedings{Li_2025_CVPR,
author = {Li, Chaoyu and Im, Eun Woo and Fazli, Pooyan},
title = {VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13723-13733}
}
|
Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building on the observation that MLLM visual encoders often fail to distinguish visually different yet semantically similar video pairs, we introduce VidHalluc, the largest benchmark designed to examine hallucinations in MLLMs for video understanding. It consists of 5,002 videos, paired to highlight cases prone to hallucinations. VidHalluc assesses hallucinations across three critical dimensions: (1) action, (2) temporal sequence, and (3) scene transition. Comprehensive testing shows that most MLLMs are vulnerable to hallucinations across these dimensions. Furthermore, we propose DINO-HEAL, a training-free method that reduces hallucinations by incorporating spatial saliency from DINOv2 to reweight visual features during inference. Our results show that DINO-HEAL consistently improves performance on VidHalluc, achieving an average improvement of 3.02% in mitigating hallucinations across all tasks. Both the VidHalluc benchmark and DINO-HEAL code are available at https://people-robots.github.io/vidhalluc.
|
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] |
518 |
StageDesigner: Artistic Stage Generation for Scenography via Theater Scripts
|
[
"Zhaoxing Gan",
"Mengtian Li",
"Ruhua Chen",
"Zhongxia Ji",
"Sichen Guo",
"Huanling Hu",
"Guangnan Ye",
"Zuo Hu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gan_StageDesigner_Artistic_Stage_Generation_for_Scenography_via_Theater_Scripts_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gan_StageDesigner_Artistic_Stage_Generation_for_Scenography_via_Theater_Scripts_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gan_StageDesigner_Artistic_Stage_CVPR_2025_supplemental.pdf
|
2503.02595
|
@InProceedings{Gan_2025_CVPR,
author = {Gan, Zhaoxing and Li, Mengtian and Chen, Ruhua and Ji, Zhongxia and Guo, Sichen and Hu, Huanling and Ye, Guangnan and Hu, Zuo},
title = {StageDesigner: Artistic Stage Generation for Scenography via Theater Scripts},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28705-28714}
}
|
In this work, we introduce StageDesigner, the first comprehensive framework for artistic stage generation using large language models combined with layout-controlled diffusion models. Given the professional requirements of stage scenography, StageDesigner simulates the workflows of seasoned artists to generate immersive 3D stage scenes. Specifically, our approach is divided into three primary modules: Script Analysis, which extracts thematic and spatial cues from input scripts; Foreground Generation, which constructs and arranges essential 3D objects; and Background Generation, which produces a harmonious background aligned with the narrative atmosphere and maintains spatial coherence by managing occlusions between foreground and background elements. Furthermore, we introduce the StagePro-V1 dataset, a dedicated dataset with 276 unique stage scenes spanning different historical styles and annotated with scripts, images, and detailed 3D layouts, specifically tailored for this task. Finally, evaluations using both standard and newly proposed metrics, along with extensive user studies, demonstrate the effectiveness of StageDesigner, showcasing its ability to produce visually and thematically cohesive stages that meet both artistic and spatial coherence standards. Project can be found at: https://deadsmither5.github.io/2025/01/03/StageDesigner/
|
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519 |
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
|
[
"Yan Jiang",
"Hao Yu",
"Xu Cheng",
"Haoyu Chen",
"Zhaodong Sun",
"Guoying Zhao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_From_Laboratory_to_Real_World_A_New_Benchmark_Towards_Privacy-Preserved_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_From_Laboratory_to_Real_World_A_New_Benchmark_Towards_Privacy-Preserved_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_From_Laboratory_to_CVPR_2025_supplemental.pdf
|
2503.12232
|
@InProceedings{Jiang_2025_CVPR,
author = {Jiang, Yan and Yu, Hao and Cheng, Xu and Chen, Haoyu and Sun, Zhaodong and Zhao, Guoying},
title = {From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8828-8837}
}
|
Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensive research attention and achieved promising results. However, in real-world surveillance contexts, data is distributed across multiple devices/entities, raising privacy and ownership concerns that make existing centralized training impractical for VI-ReID. To tackle these challenges, we propose L2RW, a benchmark that brings VI-ReID closer to real-world applications. The rationale of L2RW is that integrating decentralized training into VI-ReID can address privacy concerns in scenarios with limited data-sharing regulation. Specifically, we design protocols and corresponding algorithms for different privacy sensitivity levels. In our new benchmark, we ensure the model training is done in the conditions that: 1) data from each camera remains completely isolated, or 2) different data entities (e.g., data controllers of a certain region) can selectively share the data. In this way, we simulate scenarios with strict privacy constraints which is closer to real-world conditions. Intensive experiments with various server-side federated algorithms are conducted, showing the feasibility of decentralized VI-ReID training. Notably, when evaluated in unseen domains (i.e., new data entities), our L2RW, trained with isolated data (privacy-preserved), achieves performance comparable to SOTAs trained with shared data (privacy-unrestricted). We hope this work offers a novel research entry for deploying VI-ReID that fits real-world scenarios and can benefit the community.
|
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] |
520 |
4Deform: Neural Surface Deformation for Robust Shape Interpolation
|
[
"Lu Sang",
"Zehranaz Canfes",
"Dongliang Cao",
"Riccardo Marin",
"Florian Bernard",
"Daniel Cremers"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Sang_4Deform_Neural_Surface_Deformation_for_Robust_Shape_Interpolation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Sang_4Deform_Neural_Surface_Deformation_for_Robust_Shape_Interpolation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sang_4Deform_Neural_Surface_CVPR_2025_supplemental.pdf
|
2502.20208
|
@InProceedings{Sang_2025_CVPR,
author = {Sang, Lu and Canfes, Zehranaz and Cao, Dongliang and Marin, Riccardo and Bernard, Florian and Cremers, Daniel},
title = {4Deform: Neural Surface Deformation for Robust Shape Interpolation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6542-6551}
}
|
Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where temporal consistency across frames is lacking, and topologies are changing. Most interpolation methods are designed for structured data (i.e., meshes) and do not apply to real-world point clouds. In contrast, our approach leverages neural implicit representation (NIR) to enable free-topology changing shape deformation. Unlike previous mesh-based methods, which model learns vertex-based deformation fields, our method learns a continuous velocity field in Euclidean space, making it suitable for less structured data such as point clouds.Additionally, our method does not require intermediate-shape supervision during training; instead, we incorporate physical and geometrical constraints to regularize the velocity field. We reconstruct intermediate surfaces using a modified level-set equation, directly linking our NIR with the velocity field. Experiments show that our method significantly outperforms previous NIR approaches across various scenarios (e.g., noisy, partial, topology-changing, non-isometric shapes) and, for the first time, enables new applications like 4D Kinect sequence upsampling and real-world high-resolution mesh deformation.
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] |
521 |
Dense Match Summarization for Faster Two-view Estimation
|
[
"Jonathan Astermark",
"Anders Heyden",
"Viktor Larsson"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Astermark_Dense_Match_Summarization_for_Faster_Two-view_Estimation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Astermark_Dense_Match_Summarization_for_Faster_Two-view_Estimation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Astermark_Dense_Match_Summarization_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Astermark_2025_CVPR,
author = {Astermark, Jonathan and Heyden, Anders and Larsson, Viktor},
title = {Dense Match Summarization for Faster Two-view Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1093-1102}
}
|
In this paper, we speed up robust two-view relative pose from dense correspondences. Previous work has shown that dense matchers can significantly improve both accuracy and robustness in the resulting pose. However, the large number of matches comes with a significantly increased runtime during robust estimation in RANSAC. To avoid this, we propose an efficient match summarization scheme which provides comparable accuracy to using the full set of dense matches, while having 10-100x faster runtime. We validate our approach on standard benchmark datasets together with multiple state-of-the-art dense matchers.
|
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] |
522 |
Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing
|
[
"Shengzhi Wang",
"Yingkang Zhong",
"Jiangchuan Mu",
"Kai Wu",
"Mingliang Xiong",
"Wen Fang",
"Mingqing Liu",
"Hao Deng",
"Bin He",
"Gang Li",
"Qingwen Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Align-A-Video_Deterministic_Reward_Tuning_of_Image_Diffusion_Models_for_Consistent_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Align-A-Video_Deterministic_Reward_Tuning_of_Image_Diffusion_Models_for_Consistent_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Align-A-Video_Deterministic_Reward_CVPR_2025_supplemental.zip
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Shengzhi and Zhong, Yingkang and Mu, Jiangchuan and Wu, Kai and Xiong, Mingliang and Fang, Wen and Liu, Mingqing and Deng, Hao and He, Bin and Li, Gang and Liu, Qingwen},
title = {Align-A-Video: Deterministic Reward Tuning of Image Diffusion Models for Consistent Video Editing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2074-2083}
}
|
Due to control limitations in the denoising process and the lack of training, zero-shot video editing methods often struggle to meet user instructions, resulting in generated videos that are visually unappealing and fail to fully satisfy expectations. To address this problem, we propose Align-A-Video, a video editing pipeline that incorporates human feedback through reward fine-tuning. Our approach consists of two key steps: 1) Deterministic Reward Fine-tuning. To reduce optimization costs for expected noise distributions, we propose a deterministic reward tuning strategy. This method improves tuning stability by increasing sample determinism, allowing the tuning process to be completed in minutes; 2) Feature Propagation Across Frames. We optimize a selected anchor frame and propagate its features to the remaining frames, improving both visual quality and semantic fidelity. This approach avoids temporal consistency degradation from reward optimization. Extensive qualitative and quantitative experiments confirm the effectiveness of using reward fine-tuning in Align-A-Video, significantly improving the overall quality of generated videos.
|
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] |
523 |
Interpreting Object-level Foundation Models via Visual Precision Search
|
[
"Ruoyu Chen",
"Siyuan Liang",
"Jingzhi Li",
"Shiming Liu",
"Maosen Li",
"Zhen Huang",
"Hua Zhang",
"Xiaochun Cao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Interpreting_Object-level_Foundation_Models_via_Visual_Precision_Search_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Interpreting_Object-level_Foundation_Models_via_Visual_Precision_Search_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Interpreting_Object-level_Foundation_CVPR_2025_supplemental.pdf
|
2411.16198
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Ruoyu and Liang, Siyuan and Li, Jingzhi and Liu, Shiming and Li, Maosen and Huang, Zhen and Zhang, Hua and Cao, Xiaochun},
title = {Interpreting Object-level Foundation Models via Visual Precision Search},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30042-30052}
}
|
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. However, interpreting these models' decisions has grown increasingly challenging. Existing interpretable attribution methods for object-level task interpretation have notable limitations: (1) gradient-based methods lack precise localization due to visual-textual fusion in foundation models, and (2) perturbation-based methods produce noisy saliency maps, limiting fine-grained interpretability. To address these, we propose a Visual Precision Search method that generates accurate attribution maps with fewer regions. Our method bypasses internal model parameters to overcome attribution issues from multimodal fusion, dividing inputs into sparse sub-regions and using consistency and collaboration scores to accurately identify critical decision-making regions. We also conducted a theoretical analysis of the boundary guarantees and scope of applicability of our method. Experiments on RefCOCO, MS COCO, and LVIS show our approach enhances object-level task interpretability over SOTA for Grounding DINO and Florence-2 across various evaluation metrics, with faithfulness gains of 23.7%, 31.6%, and 20.1% on MS COCO, LVIS, and RefCOCO for Grounding DINO, and 50.7% and 66.9% on MS COCO and RefCOCO for Florence-2. Additionally, our method can interpret failures in visual grounding and object detection tasks, surpassing existing methods across multiple evaluation metrics. The code is released at https://github.com/RuoyuChen10/VPS.
|
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] |
524 |
Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows
|
[
"Shentong Mo",
"Yibing Song"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Mo_Foley-Flow_Coordinated_Video-to-Audio_Generation_with_Masked_Audio-Visual_Alignment_and_Dynamic_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Mo_Foley-Flow_Coordinated_Video-to-Audio_Generation_with_Masked_Audio-Visual_Alignment_and_Dynamic_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Mo_2025_CVPR,
author = {Mo, Shentong and Song, Yibing},
title = {Foley-Flow: Coordinated Video-to-Audio Generation with Masked Audio-Visual Alignment and Dynamic Conditional Flows},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28912-28921}
}
|
Coordinated audio generation based on video inputs typically requires a strict audio-visual (AV) alignment, where both semantics and rhythmics of the generated audio segments shall correspond to those in the video frames. Previous studies leverage a two-stage design where the AV encoders are firstly aligned via contrastive learning, then the encoded video representations guide the audio generation process. We observe that both contrastive learning and global video guidance are effective in aligning overall AV semantics while limiting temporally rhythmic synchronization. In this work, we propose Foley-Flow to first align unimodal AV encoders via masked modeling training, where the masked audio segments are recovered under the guidance of the corresponding video segments. After training, the AV encoders which are separately pretrained using only unimodal data are aligned with semantic and rhythmic consistency. Then, we develop a dynamic conditional flow for the final audio generation. Built upon the efficient velocity flow generation framework, our dynamic conditional flow utilizes temporally varying video features as the dynamic condition to guide corresponding audio segment generations. To this end, we extract coherent semantic and rhythmic representations during masked AV alignment, and use this representation of video segments to guide audio generation temporally. Our audio results are evaluated on the standard benchmarks and largely surpass existing results under several metrics. The superior performance indicates that Foley-Flow is effective in generating coordinated audios that are both semantically and rhythmically coherent to various video sequences.
|
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] |
525 |
LION-FS: Fast & Slow Video-Language Thinker as Online Video Assistant
|
[
"Wei Li",
"Bing Hu",
"Rui Shao",
"Leyang Shen",
"Liqiang Nie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LION-FS_Fast__Slow_Video-Language_Thinker_as_Online_Video_Assistant_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LION-FS_Fast__Slow_Video-Language_Thinker_as_Online_Video_Assistant_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LION-FS_Fast__CVPR_2025_supplemental.pdf
| null |
@InProceedings{Li_2025_CVPR,
author = {Li, Wei and Hu, Bing and Shao, Rui and Shen, Leyang and Nie, Liqiang},
title = {LION-FS: Fast \& Slow Video-Language Thinker as Online Video Assistant},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3240-3251}
}
|
First-person video assistants are highly anticipated to enhance our daily life through online video dialogue. However, existing online video assistants often sacrifice assistant efficacy for real-time efficiency by processing low-frame-rate videos with coarse-grained visual features. To overcome the trade-off between efficacy and efficiency, we propose "**F**ast & **S**low Video-Language Thinker" as on**LI**ne vide**O** assista**N**t, **LION-FS**, achieving real-time, proactive, temporally accurate, and contextually precise responses. LION-FS adopts a two-stage optimization strategy: **1) Fast Path: Routing-Based Response Determination** evaluates frame-by-frame whether a immediate response is necessary. To enhance responses determination accuracy and handle higher frame-rate inputs efficiently, we employ Token Aggregation Routing to dynamically fuse spatiotemporal features without increasing token numbers, while utilizing Token Dropping Routing to eliminate redundant features, and **2) Slow Path: Multi-granularity Keyframe Augmentation** optimizes keyframes during response generation. To provide comprehensive and detailed responses beyond atomic actions constrained by training data, fine-grained spatial features and human-environment interaction features are extracted through multi-granular pooling. They are further integrated into a meticulously designed multimodal Thinking Template to guide more precise response generation. Comprehensive evaluations on online video tasks demonstrate that LION-FS achieves state-of-the-art efficacy and efficiency. The codes will be released soon.
|
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] |
526 |
CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
|
[
"Yuan Zhou",
"Qingshan Xu",
"Jiequan Cui",
"Junbao Zhou",
"Jing Zhang",
"Richang Hong",
"Hanwang Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_CARE_Transformer_Mobile-Friendly_Linear_Visual_Transformer_via_Decoupled_Dual_Interaction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_CARE_Transformer_Mobile-Friendly_Linear_Visual_Transformer_via_Decoupled_Dual_Interaction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_CARE_Transformer_Mobile-Friendly_CVPR_2025_supplemental.pdf
|
2411.16170
|
@InProceedings{Zhou_2025_CVPR,
author = {Zhou, Yuan and Xu, Qingshan and Cui, Jiequan and Zhou, Junbao and Zhang, Jing and Hong, Richang and Zhang, Hanwang},
title = {CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20135-20145}
}
|
Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable to be deployed in resource-constrained mobile devices, due to suffering from either few efficiency gains or significant accuracy drops. In this paper, we propose a new deCoupled duAl-interactive lineaR attEntion (CARE) mechanism, revealing that features' decoupling and interaction can fully unleash the power of linear attention. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies, thereby preserving sufficient local and global information while effectively enhancing the efficiency of models. Then, a dynamic memory unit is employed to maintain critical information along the network pipeline. Moreover, we design a dual interaction module to effectively facilitate interaction between local inductive bias and long-range information as well as among features at different layers. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy. Extensive experiments on ImageNet-1K, COCO, and ADE20K datasets demonstrate the effectiveness of our approach, e.g., achieving 78.4/82.1% top-1 accuracy on ImagegNet-1K at the cost of only 0.7/1.9 GMACs. Codes will be released on github.
|
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] |
527 |
Paint by Inpaint: Learning to Add Image Objects by Removing Them First
|
[
"Navve Wasserman",
"Noam Rotstein",
"Roy Ganz",
"Ron Kimmel"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wasserman_Paint_by_Inpaint_Learning_to_Add_Image_Objects_by_Removing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wasserman_Paint_by_Inpaint_Learning_to_Add_Image_Objects_by_Removing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wasserman_Paint_by_Inpaint_CVPR_2025_supplemental.pdf
|
2404.18212
|
@InProceedings{Wasserman_2025_CVPR,
author = {Wasserman, Navve and Rotstein, Noam and Ganz, Roy and Kimmel, Ron},
title = {Paint by Inpaint: Learning to Add Image Objects by Removing Them First},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18313-18324}
}
|
Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without requiring user-provided input masks remains a challenge. We address this by leveraging the insight that removing objects (Inpaint) is significantly simpler than its inverse process of adding them (Paint), attributed to inpainting models that benefit from segmentation mask guidance. Capitalizing on this realization, by implementing an automated and extensive pipeline, we curate a filtered large-scale image dataset containing pairs of images and their corresponding object-removed versions. Using these pairs, we train a diffusion model to inverse the inpainting process, effectively adding objects into images. Unlike other editing datasets, ours features natural target images instead of synthetic ones while ensuring source-target consistency by construction. Additionally, we utilize a large Vision-Language Model to provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions. Our quantitative and qualitative results show that the trained model surpasses existing models in both object addition and general editing tasks. Visit our project page for the released dataset and trained models: https://rotsteinnoam.github.io/Paint-by-Inpaint/.
|
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] |
528 |
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
|
[
"Andong Deng",
"Tongjia Chen",
"Shoubin Yu",
"Taojiannan Yang",
"Lincoln Spencer",
"Yapeng Tian",
"Ajmal Saeed Mian",
"Mohit Bansal",
"Chen Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Motion-Grounded_Video_Reasoning_Understanding_and_Perceiving_Motion_at_Pixel_Level_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Motion-Grounded_Video_Reasoning_Understanding_and_Perceiving_Motion_at_Pixel_Level_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Motion-Grounded_Video_Reasoning_CVPR_2025_supplemental.pdf
|
2411.09921
|
@InProceedings{Deng_2025_CVPR,
author = {Deng, Andong and Chen, Tongjia and Yu, Shoubin and Yang, Taojiannan and Spencer, Lincoln and Tian, Yapeng and Mian, Ajmal Saeed and Bansal, Mohit and Chen, Chen},
title = {Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8625-8636}
}
|
In this paper, we introduce Motion-Grounded Video Reasoning, a new motionunderstanding task that requires generating visual answers (video segmentationmasks) according to the input question, and hence needs implicit spatiotemporalreasoning and grounding. This task extends existing spatiotemporal groundingwork focusing on explicit action/motion grounding, to a more general format byenabling implicit reasoning via questions. To facilitate the development of the newtask, we collect a large-scale dataset called GROUNDMORE, which comprises1,715 video clips, 249K object masks that are deliberately designed with 4 questiontypes (Causal, Sequential, Counterfactual, and Descriptive) for benchmarkingdeep and comprehensive motion reasoning abilities. GROUNDMORE uniquelyrequires models to generate visual answers, providing a more concrete and visuallyinterpretable response than plain texts. It evaluates models on both spatiotemporalgrounding and reasoning, fostering to address complex challenges in motion-relatedvideo reasoning, temporal perception, and pixel-level understanding. Furthermore,we introduce a novel baseline model named Motion-Grounded Video ReasoningAssistant (MORA). MORA incorporates the multimodal reasoning ability from theMultimodal LLM, the pixel-level perception capability from the grounding model(SAM), and the temporal perception ability from a lightweight localization head.MORA achieves respectable performance on GROUNDMORE outperforming thebest existing visual grounding baseline model by an average of 21.5% relatively.We hope this novel and challenging task will pave the way for future advancementsin robust and general motion understanding via video reasoning segmentation.
|
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] |
529 |
PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
|
[
"Yaohua Zha",
"Yanzi Wang",
"Hang Guo",
"Jinpeng Wang",
"Tao Dai",
"Bin Chen",
"Zhihao Ouyang",
"Xue Yuerong",
"Ke Chen",
"Shu-Tao Xia"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zha_PMA_Towards_Parameter-Efficient_Point_Cloud_Understanding_via_Point_Mamba_Adapter_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zha_PMA_Towards_Parameter-Efficient_Point_Cloud_Understanding_via_Point_Mamba_Adapter_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zha_PMA_Towards_Parameter-Efficient_CVPR_2025_supplemental.pdf
|
2505.20941
|
@InProceedings{Zha_2025_CVPR,
author = {Zha, Yaohua and Wang, Yanzi and Guo, Hang and Wang, Jinpeng and Dai, Tao and Chen, Bin and Ouyang, Zhihao and Yuerong, Xue and Chen, Ke and Xia, Shu-Tao},
title = {PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16976-16986}
}
|
Applying pre-trained models to assist point cloud understanding has recently become a mainstream paradigm in 3D perception. However, existing application strategies are straightforward, utilizing only the final output of the pre-trained model for various task heads. It neglects the rich complementary information in the intermediate layer, thereby failing to fully unlock the potential of pre-trained models. To overcome this limitation, we propose an orthogonal solution: Point Mamba Adapter (PMA), which constructs an ordered feature sequence from all layers of the pre-trained model and leverages Mamba to fuse all complementary semantics, thereby promoting comprehensive point cloud understanding. Constructing this ordered sequence is non-trivial due to the inherent isotropy of 3D space. Therefore, we further propose a geometry-constrained gate prompt generator (G2PG) shared across different layers, which applies shared geometric constraints to the output gates of the Mamba and dynamically optimizes the spatial order, thus enabling more effective integration of multi-layer information. Extensive experiments conducted on challenging point cloud datasets across various tasks demonstrate that our PMA elevates the capability for point cloud understanding to a new level by fusing diverse complementary intermediate features. Code is available at https://github.com/zyh16143998882/PMA.
|
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] |
530 |
All-directional Disparity Estimation for Real-world QPD Images
|
[
"Hongtao Yu",
"Shaohui Song",
"Lihu Sun",
"Wenkai Su",
"Xiaodong Yang",
"Chengming Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_All-directional_Disparity_Estimation_for_Real-world_QPD_Images_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_All-directional_Disparity_Estimation_for_Real-world_QPD_Images_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_All-directional_Disparity_Estimation_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Yu_2025_CVPR,
author = {Yu, Hongtao and Song, Shaohui and Sun, Lihu and Su, Wenkai and Yang, Xiaodong and Liu, Chengming},
title = {All-directional Disparity Estimation for Real-world QPD Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21836-21846}
}
|
Quad Photodiode (QPD) sensors represent an evolution by providing four sub-views, whereas dual-pixel (DP) sensors are limited to two sub-views. In addition to enhancing auto-focus performance, QPD sensors also enable disparity estimation in horizontal and vertical directions. However, the characteristics of QPD sensors, including uneven illumination across sub-views and the narrow baseline, render algorithm design difficult. Furthermore, effectively utilizing the two-directional disparity of QPD sensors remains a challenge. The scarcity of QPD disparity datasets also limits the development of learning-based methods. In this work, we address these challenges by first proposing a DPNet for DP disparity estimation. Specifically, we design an illumination-invariant module to reduce the impact of illumination, followed by a coarse-to-fine module to estimate sub-pixel disparity. Building upon the DPNet, we further propose a QuadNet, which integrates the two-directional disparity via an edge-aware fusion module. To facilitate the evaluation of our approaches, we propose the first QPD disparity dataset QPD2K, comprising 2,100 real-world QPD images and corresponding disparity maps. Experiments demonstrate that our approaches achieve state-of-the-art performance in DP and QPD disparity estimation.
|
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] |
531 |
LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation
|
[
"Min Wu Jeong",
"Chae Eun Rhee"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_LC-Mamba_Local_and_Continuous_Mamba_with_Shifted_Windows_for_Frame_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_LC-Mamba_Local_and_Continuous_Mamba_with_Shifted_Windows_for_Frame_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_LC-Mamba_Local_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Jeong_2025_CVPR,
author = {Jeong, Min Wu and Rhee, Chae Eun},
title = {LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17671-17681}
}
|
In this paper, we propose LC-Mamba, a Mamba-based model that captures fine-grained spatiotemporal information in video frames, addressing limitations in current interpolation methods and enhancing performance. The main contributions are as follows: First, we apply a shifted local window technique to reduce historical decay and enhance local spatial features, allowing multiscale capture of detailed motion between frames. Second, we introduce a Hilbert curve-based selective state scan to maintain continuity across window boundaries, preserving spatial correlations both within and between windows. Third, we extend the Hilbert curve to enable voxel-level scanning to effectively capture spatiotemporal characteristics between frames. The proposed LC-Mamba achieves competitive results, with a PSNR of 36.53 dB on Vimeo-90k, outperforming prior models by +0.03 dB. The code and models are publicly available at https://github.com/Miinuuu/LC-Mamba.git
|
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] |
532 |
Zero-Shot Head Swapping in Real-World Scenarios
|
[
"Taewoong Kang",
"Sohyun Jeong",
"Hyojin Jang",
"Jaegul Choo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_Zero-Shot_Head_Swapping_in_Real-World_Scenarios_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_Zero-Shot_Head_Swapping_in_Real-World_Scenarios_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_Zero-Shot_Head_Swapping_CVPR_2025_supplemental.pdf
|
2503.00861
|
@InProceedings{Kang_2025_CVPR,
author = {Kang, Taewoong and Jeong, Sohyun and Jang, Hyojin and Choo, Jaegul},
title = {Zero-Shot Head Swapping in Real-World Scenarios},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10805-10814}
}
|
With growing demand in media and social networks for personalized images, the need for advanced head-swapping techniques--integrating an entire head from the head image with the body from the body image--has increased. However, traditional head-swapping methods heavily rely on face-centered cropped data with primarily frontal-facing views, which limits their effectiveness in real-world applications. Additionally, their masking methods, designed to indicate regions requiring editing, are optimized for these types of dataset but struggle to achieve seamless blending in complex situations, such as when the original data includes features like long hair extending beyond the masked area. To overcome these limitations and enhance adaptability in diverse and complex scenarios, we propose a novel head swapping method, HID, that is robust to images including the full head and the upper body, and handles from frontal to side views, while automatically generating context-aware masks. For automatic mask generation, we introduce the IOMask, which enables seamless blending of the head and body, effectively addressing integration challenges. We further introduce the hair injection module to capture hair details with greater precision. Our experiments demonstrate that the proposed approach achieves state-of-the-art performance in head swapping, providing visually consistent and realistic results across a wide range of challenging conditions.
|
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] |
533 |
Toward Robust Neural Reconstruction from Sparse Point Sets
|
[
"Amine Ouasfi",
"Shubhendu Jena",
"Eric Marchand",
"Adnane Boukhayma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ouasfi_Toward_Robust_Neural_Reconstruction_from_Sparse_Point_Sets_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ouasfi_Toward_Robust_Neural_Reconstruction_from_Sparse_Point_Sets_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ouasfi_Toward_Robust_Neural_CVPR_2025_supplemental.pdf
|
2412.16361
|
@InProceedings{Ouasfi_2025_CVPR,
author = {Ouasfi, Amine and Jena, Shubhendu and Marchand, Eric and Boukhayma, Adnane},
title = {Toward Robust Neural Reconstruction from Sparse Point Sets},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6552-6562}
}
|
We consider the challenging problem of learning Signed Distance Functions (SDF) from sparse and noisy 3D point clouds. In contrast to recent methods that depend on smoothness priors, our method, rooted in a distributionally robust optimization (DRO) framework, incorporates a regularization term that leverages samples from the uncertainty regions of the model to improve the learned SDFs. Thanks to tractable dual formulations, we show that this framework enables a stable and efficient optimization of SDFs in the absence of ground truth supervision. Using a variety of synthetic and real data evaluations from different modalities, we show that of our DRO based learning framework can improve SDF learning with respect to baselines and the state-of-the-art.
|
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] |
534 |
GPAvatar: High-fidelity Head Avatars by Learning Efficient Gaussian Projections
|
[
"Wei-Qi Feng",
"Dong Han",
"Ze-Kang Zhou",
"Shunkai Li",
"Xiaoqiang Liu",
"Pengfei Wan",
"Di Zhang",
"Miao Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_GPAvatar_High-fidelity_Head_Avatars_by_Learning_Efficient_Gaussian_Projections_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_GPAvatar_High-fidelity_Head_Avatars_by_Learning_Efficient_Gaussian_Projections_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_GPAvatar_High-fidelity_Head_CVPR_2025_supplemental.zip
| null |
@InProceedings{Feng_2025_CVPR,
author = {Feng, Wei-Qi and Han, Dong and Zhou, Ze-Kang and Li, Shunkai and Liu, Xiaoqiang and Wan, Pengfei and Zhang, Di and Wang, Miao},
title = {GPAvatar: High-fidelity Head Avatars by Learning Efficient Gaussian Projections},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {250-259}
}
|
Existing radiance field-based head avatar methods have mostly relied on pre-computed explicit priors (e.g., mesh, point) or neural implicit representations, making it challenging to achieve high fidelity with both computational efficiency and low memory consumption. To overcome this, we present GPAvatar, a novel and efficient Gaussian splatting-based method for reconstructing high-fidelity dynamic 3D head avatars from monocular videos. We extend Gaussians in 3D space to a high-dimensional embedding space encompassing Gaussian's spatial position and avatar expression, enabling the representation of the head avatar with arbitrary pose and expression. To enable splatting-based rasterization, a linear transformation is learned to project each high-dimensional Gaussian back to the 3D space, which is sufficient to capture expression variations instead of using complex neural networks. Furthermore, we propose an adaptive densification strategy that dynamically allocates Gaussians to regions with high expression variance, improving the facial detail representation. Experimental results on three datasets show that our method outperforms existing state-of-the-art methods in rendering quality and speed while reducing memory usage in training and rendering.
|
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] |
535 |
PIAD: Pose and Illumination agnostic Anomaly Detection
|
[
"Kaichen Yang",
"Junjie Cao",
"Zeyu Bai",
"Zhixun Su",
"Andrea Tagliasacchi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_PIAD_Pose_and_Illumination_agnostic_Anomaly_Detection_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_PIAD_Pose_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Yang_2025_CVPR,
author = {Yang, Kaichen and Cao, Junjie and Bai, Zeyu and Su, Zhixun and Tagliasacchi, Andrea},
title = {PIAD: Pose and Illumination agnostic Anomaly Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4734-4743}
}
|
We introduce the Pose and Illumination agnostic Anomaly Detection (PIAD) problem, a generalization of pose-agnostic anomaly detection (PAD). Being illumination agnostic is critical, as it relaxes the assumption that training data for an object has to be acquired in the same light configuration of the query images that we want to test. Moreover, even if the object is placed within the same capture environment, being illumination agnostic implies that we can relax the assumption that the relative pose between environment light and query object has to match the one in the training data. We introduce a new dataset to study this problem, containing both synthetic and real-world examples, propose a new baseline for PIAD, and demonstrate how our baseline provides state-of-the-art results in both PAD and PIAD, not only in the new proposed dataset, but also in existing datasets that were designed for the simpler PAD problem. Project page: https://kaichen-yang.github.io/piad/.
|
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] |
536 |
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
|
[
"Edson Araujo",
"Andrew Rouditchenko",
"Yuan Gong",
"Saurabhchand Bhati",
"Samuel Thomas",
"Brian Kingsbury",
"Leonid Karlinsky",
"Rogerio Feris",
"James R. Glass",
"Hilde Kuehne"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Araujo_CAV-MAE_Sync_Improving_Contrastive_Audio-Visual_Mask_Autoencoders_via_Fine-Grained_Alignment_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Araujo_CAV-MAE_Sync_Improving_Contrastive_Audio-Visual_Mask_Autoencoders_via_Fine-Grained_Alignment_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Araujo_CAV-MAE_Sync_Improving_CVPR_2025_supplemental.pdf
|
2505.01237
|
@InProceedings{Araujo_2025_CVPR,
author = {Araujo, Edson and Rouditchenko, Andrew and Gong, Yuan and Bhati, Saurabhchand and Thomas, Samuel and Kingsbury, Brian and Karlinsky, Leonid and Feris, Rogerio and Glass, James R. and Kuehne, Hilde},
title = {CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18794-18803}
}
|
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations that fail to capture fine-grained temporal correspondences with visual frames.Additionally, existing methods often struggle with conflicting optimization objectives when trying to jointly learn reconstruction and cross-modal alignment. In this work, we propose CAV-MAE Sync as a simple yet effective extension of the original CAV-MAE framework for self-supervised audio-visual learning. We address three key challenges: First, we tackle the granularity mismatch between modalities by treating audio as a temporal sequence aligned with video frames, rather than using global representations. Second, we resolve conflicting optimization goals by separating contrastive and reconstruction objectives through dedicated global tokens. Third, we improve spatial localization by introducing learnable register tokens that reduce semantic load on patch tokens. We evaluate the proposed approach on AudioSet, VGG Sound, and the ADE20K Sound dataset on zero-shot retrieval, classification and localization tasks demonstrating state-of-the-art performance and outperforming more complex architectures. Code available at https://github.com/edsonroteia/cav-mae-sync.
|
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] |
537 |
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
|
[
"Yoojin Jung",
"Byung Cheol Song"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_Two_is_Better_than_One__Efficient_Ensemble_Defense_for_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_Two_is_Better_than_One__Efficient_Ensemble_Defense_for_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_Two_is_Better_CVPR_2025_supplemental.pdf
|
2504.04747
|
@InProceedings{Jung_2025_CVPR,
author = {Jung, Yoojin and Song, Byung Cheol},
title = {Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9696-9706}
}
|
Deep learning-based computer vision systems adopt complex and large architectures to improve performance, yet they face challenges in deployment on resource-constrained mobile and edge devices. To address this issue, model compression techniques such as pruning, quantization, and matrix factorization have been proposed; however, these compressed models are often highly vulnerable to adversarial attacks. We introduce the Efficient Ensemble Defense (EED) technique, which diversifies the compression of a single base model based on different pruning importance scores and enhances ensemble diversity to achieve high adversarial robustness and resource efficiency. EED dynamically determines the number of necessary sub-models during the inference stage, minimizing unnecessary computations while maintaining high robustness. On the CIFAR-10 and SVHN datasets, EED demonstrated state-of-the-art robustness performance compared to existing adversarial pruning techniques, along with an inference speed improvement of up to 1.86 times. This proves that EED is a powerful defense solution in resource-constrained environments.
|
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] |
538 |
Tiled Diffusion
|
[
"Or Madar",
"Ohad Fried"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Madar_Tiled_Diffusion_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Madar_Tiled_Diffusion_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Madar_Tiled_Diffusion_CVPR_2025_supplemental.zip
|
2412.15185
|
@InProceedings{Madar_2025_CVPR,
author = {Madar, Or and Fried, Ohad},
title = {Tiled Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7795-7804}
}
|
Image tiling--the seamless connection of disparate images to create a coherent visual field--is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains.This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images.Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360deg synthesis.
|
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] |
539 |
Using Diffusion Priors for Video Amodal Segmentation
|
[
"Kaihua Chen",
"Deva Ramanan",
"Tarasha Khurana"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Using_Diffusion_Priors_for_Video_Amodal_Segmentation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Using_Diffusion_Priors_for_Video_Amodal_Segmentation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Using_Diffusion_Priors_CVPR_2025_supplemental.zip
|
2412.04623
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Kaihua and Ramanan, Deva and Khurana, Tarasha},
title = {Using Diffusion Priors for Video Amodal Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22890-22900}
}
|
Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object segmentation do not account for this amodal nature of the world, and only work for segmentation of visible or modal objects. Few amodal methods exist; single-image segmentation methods cannot handle high-levels of occlusions which are better inferred using temporal information, and multi-frame methods have focused solely on segmenting rigid objects. To this end, we propose to tackle video amodal segmentation by formulating it as a conditional generation task, thereby capitalizing on the foundational knowledge in video generative models. Our method is simple; we repurpose these models to condition on a sequence of modal mask frames of an object along with contextual depth maps, to learn which object boundary may be occluded and therefore, extended to hallucinate the complete extent of an object. This is followed by a content completion stage which is able to inpaint the occluded regions of an object. We benchmark our approach alongside a wide array of state-of-the-art methods on four datasets and show a dramatic improvement of upto 13% for amodal segmentation in an object's occluded region.
|
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] |
540 |
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation
|
[
"Arnav M. Das",
"Gantavya Bhatt",
"Lilly Kumari",
"Sahil Verma",
"Jeff Bilmes"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Das_COBRA_COmBinatorial_Retrieval_Augmentation_for_Few-Shot_Adaptation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Das_COBRA_COmBinatorial_Retrieval_Augmentation_for_Few-Shot_Adaptation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Das_COBRA_COmBinatorial_Retrieval_CVPR_2025_supplemental.pdf
|
2412.17684
|
@InProceedings{Das_2025_CVPR,
author = {Das, Arnav M. and Bhatt, Gantavya and Kumari, Lilly and Verma, Sahil and Bilmes, Jeff},
title = {COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20534-20546}
}
|
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task. However, these approaches are prone to selecting highly redundant samples, since they fail to incorporate any notion of diversity. In our work, we first demonstrate that data selection strategies used in prior retrieval-augmented few-shot adaptation settings can be generalized using a class of functions known as Combinatorial Mutual Information (CMI) measures. We then propose COBRA (COmBinatorial Retrieval Augmentation), which employs an alternative CMI measure that considers both diversity and similarity to a target dataset. COBRA consistently outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques when used to retrieve samples from LAION-2B. COBRA introduces negligible computational overhead to the cost of retrieval while providing significant gains in downstream model performance.
|
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] |
541 |
Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera
|
[
"Zhengdi Yu",
"Stefanos Zafeiriou",
"Tolga Birdal"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Dyn-HaMR_Recovering_4D_Interacting_Hand_Motion_from_a_Dynamic_Camera_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Dyn-HaMR_Recovering_4D_CVPR_2025_supplemental.zip
| null |
@InProceedings{Yu_2025_CVPR,
author = {Yu, Zhengdi and Zafeiriou, Stefanos and Birdal, Tolga},
title = {Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Camera},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27716-27726}
}
|
We propose Dyn-HaMR, to the best of our knowledge, the first approach to reconstruct 4D global hand motion from monocular videos recorded by dynamic cameras in the wild. Reconstructing accurate 3D hand meshes from monocular videos is a crucial task for understanding human behaviour, with significant applications in augmented and virtual reality (AR/VR). However, existing methods for monocular hand reconstruction typically rely on a weak perspective camera model, which simulates hand motion within a limited camera frustum. As a result, these approaches struggle to recover the full 3D global trajectory and often produce noisy or incorrect depth estimations, particularly when the video is captured by dynamic or moving cameras, which is common in egocentric scenarios. Our \name consists of a multi-stage, multi-objective optimization pipeline, that factors in (i) simultaneous localization and mapping (SLAM) to robustly estimate relative camera motion, (ii) an interacting-hand prior for generative infilling and to refine the interaction dynamics, ensuring plausible recovery under (self-)occlusions, and (iii) hierarchical initialization through a combination of state-of-the-art hand tracking methods.
|
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] |
542 |
The Scene Language: Representing Scenes with Programs, Words, and Embeddings
|
[
"Yunzhi Zhang",
"Zizhang Li",
"Matt Zhou",
"Shangzhe Wu",
"Jiajun Wu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_The_Scene_Language_Representing_Scenes_with_Programs_Words_and_Embeddings_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_The_Scene_Language_Representing_Scenes_with_Programs_Words_and_Embeddings_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_The_Scene_Language_CVPR_2025_supplemental.pdf
|
2410.16770
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Yunzhi and Li, Zizhang and Zhou, Matt and Wu, Shangzhe and Wu, Jiajun},
title = {The Scene Language: Representing Scenes with Programs, Words, and Embeddings},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24625-24634}
}
|
We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene with three key components: a program that specifies the hierarchical and relational structure of entities in the scene, words in natural language that summarize the semantic class of each entity, and embeddings that capture the visual identity of each entity. This representation can be inferred from pre-trained language models via a training-free inference technique, given text or image inputs. The resulting scene can be rendered into images using traditional, neural, or hybrid graphics renderers. Together, this forms an automated system for high-quality 3D and 4D scene generation. Compared with existing representations like scene graphs, our proposed Scene Language generates complex scenes with higher fidelity, while explicitly modeling the scene structures to enable precise control and editing.
|
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] |
543 |
ProbeSDF: Light Field Probes For Neural Surface Reconstruction
|
[
"Briac Toussaint",
"Diego Thomas",
"Jean-Sébastien Franco"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Toussaint_ProbeSDF_Light_Field_Probes_For_Neural_Surface_Reconstruction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Toussaint_ProbeSDF_Light_Field_Probes_For_Neural_Surface_Reconstruction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Toussaint_ProbeSDF_Light_Field_CVPR_2025_supplemental.pdf
|
2412.10084
|
@InProceedings{Toussaint_2025_CVPR,
author = {Toussaint, Briac and Thomas, Diego and Franco, Jean-S\'ebastien},
title = {ProbeSDF: Light Field Probes For Neural Surface Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11026-11035}
}
|
SDF-based differential rendering frameworks have achieved state-of-the-art multiview 3D shape reconstruction. In this work, we re-examine this family of approaches by minimally reformulating its core appearance model in a way that simultaneously yields faster computation and increased performance. To this goal, we exhibit a physically-inspired minimal radiance parametrization decoupling angular and spatial contributions, by encoding them with a small number of features stored in two respective volumetric grids of different resolutions. Requiring as little as four parameters per voxel, and a tiny MLP call inside a single fully fused kernel, our approach allows to enhance performance with both surface and image (PSNR) metrics, while providing a significant training speedup and real-time rendering. We show this performance to be consistently achieved on real data over two widely different and popular application fields, generic object and human subject shape reconstruction, using four representative and challenging datasets.
|
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] |
544 |
Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays
|
[
"Laurie Bose",
"Jianing Chen",
"Piotr Dudek"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bose_Descriptor-In-Pixel__Point-Feature_CVPR_2025_supplemental.zip
| null |
@InProceedings{Bose_2025_CVPR,
author = {Bose, Laurie and Chen, Jianing and Dudek, Piotr},
title = {Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5392-5400}
}
|
This paper presents a novel approach for joint point-feature detection and tracking, designed specifically for Pixel Processor Array (PPA) vision sensors. Instead of standard pixels, PPA sensors consist of thousands of "pixel-processors", enabling massive parallel computation of visual data at the point of light capture. Our approach performs all computation entirely in-pixel, meaning no raw image data need ever leave the sensor for external processing. We introduce a Descriptor-In-Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA's architecture enables the response of every processor's descriptor, upon the current image, to be computed in parallel. This produces a "descriptor response map", which, by generating the correct layout of descriptors across the pixel-processors, can be used for both point-feature detection and tracking. This reduces sensor output to just sparse feature locations and descriptors, read-out via an address-event interface, giving a greater than 1000X reduction in data transfer compared to raw image output. The sparse readout and complete utilization of all pixel-processors makes our approach very efficient. Our implementation upon the SCAMP-7 PPA prototype runs at over 3000 FPS (Frames Per Second), tracking point-features reliably under violent motion. This is the first work performing point-feature detection and tracking entirely in-pixel.
|
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] |
545 |
Hybrid Concept Bottleneck Models
|
[
"Yang Liu",
"Tianwei Zhang",
"Shi Gu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid_Concept_Bottleneck_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hybrid_Concept_Bottleneck_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Yang and Zhang, Tianwei and Gu, Shi},
title = {Hybrid Concept Bottleneck Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20179-20189}
}
|
Concept Bottleneck Models (CBMs) provide an interpretable framework for neural networks by mapping visual features to predefined, human-understandable concepts. However, the application of CBMs is often constrained by insufficient concept annotations. Recently, multi-modal pre-trained models have shown promise in reducing annotation costs by aligning visual representations with textual concept embeddings. Nevertheless, the quality and completeness of the predefined concepts significantly affect the performance of CBMs.In this work, we propose Hybrid Concept Bottleneck Model (HybridCBM), a novel CBM framework to address the challenge of incomplete predefined concepts. Our method consists of two main components: a Static Concept Bank and a Dynamic Concept Bank. The Static Concept Bank directly leverages large language models (LLMs) for concept construction, while the Dynamic Concept Bank employs learnable vectors to capture complementary and valuable concepts continuously during training. After training, a pre-trained translator converts these vectors into human-understandable concepts, further enhancing model interpretability. Notably, HybridCBM is highly flexible and can be easily applied to any CBM to improve performance. Experimental results across multiple datasets demonstrate that HybridCBM outperforms current state-of-the-art methods and achieves comparable results to black-box models. Additionally, we propose novel metrics to evaluate the quality of the learned concepts, showing that they perform comparably to predefined concepts.
|
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] |
546 |
UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping
|
[
"Aashish Rai",
"Dilin Wang",
"Mihir Jain",
"Nikolaos Sarafianos",
"Kefan Chen",
"Srinath Sridhar",
"Aayush Prakash"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rai_UVGS_Reimagining_Unstructured_CVPR_2025_supplemental.pdf
|
2502.01846
|
@InProceedings{Rai_2025_CVPR,
author = {Rai, Aashish and Wang, Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush},
title = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5927-5937}
}
|
3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation. We further find that these heterogeneous features can be compressed into a lower-dimensional (e.g., 3-channel) shared feature space using a carefully designed multi-branch network.The compressed UVGS can be treated as typical RGB images. Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians, making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. In our experiments, we demonstrate various unconditional, conditional generation, and inpainting applications of 3DGS based on diffusion models, which were previously non-trivial.
|
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] |
547 |
Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning
|
[
"Da-Wei Zhou",
"Zi-Wen Cai",
"Han-Jia Ye",
"Lijun Zhang",
"De-Chuan Zhan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Dual_Consolidation_for_Pre-Trained_Model-Based_Domain-Incremental_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Dual_Consolidation_for_Pre-Trained_Model-Based_Domain-Incremental_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Dual_Consolidation_for_CVPR_2025_supplemental.pdf
|
2410.00911
|
@InProceedings{Zhou_2025_CVPR,
author = {Zhou, Da-Wei and Cai, Zi-Wen and Ye, Han-Jia and Zhang, Lijun and Zhan, De-Chuan},
title = {Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20547-20557}
}
|
Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. While recent advances in pre-trained models provide a solid foundation for DIL, learning new concepts often results in the catastrophic forgetting of pre-trained knowledge. Specifically, sequential model updates can overwrite both the representation and the classifier with knowledge from the latest domain. Thus, it is crucial to develop a representation and corresponding classifier that accommodate all seen domains throughout the learning process. To this end, we propose DUal ConsolidaTion (Duct) to unify and consolidate historical knowledge at both the representation and classifier levels. By merging the backbone of different stages, we create a representation space suitable for multiple domains incrementally. The merged representation serves as a balanced intermediary that captures task-specific features from all seen domains. Additionally, to address the mismatch between consolidated embeddings and the classifier, we introduce an extra classifier consolidation process. Leveraging class-wise semantic information, we estimate the classifier weights of old domains within the latest embedding space. By merging historical and estimated classifiers, we align them with the consolidated embedding space, facilitating incremental classification. Extensive experimental results on four benchmark datasets demonstrate Duct's state-of-the-art performance. Code is available at: https://github.com/Estrella-fugaz/CVPR25-Duct.
|
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] |
548 |
Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References
|
[
"Yitang Li",
"Mingxian Lin",
"Zhuo Lin",
"Yipeng Deng",
"Yue Cao",
"Li Yi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Learning_Physics-Based_Full-Body_Human_Reaching_and_Grasping_from_Brief_Walking_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Learning_Physics-Based_Full-Body_Human_Reaching_and_Grasping_from_Brief_Walking_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Learning_Physics-Based_Full-Body_CVPR_2025_supplemental.zip
|
2503.07481
|
@InProceedings{Li_2025_CVPR,
author = {Li, Yitang and Lin, Mingxian and Lin, Zhuo and Deng, Yipeng and Cao, Yue and Yi, Li},
title = {Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27673-27682}
}
|
Existing motion generation methods based on mocap data are often limited by data quality and coverage. In this work, we propose a framework that generates diverse, physically feasible full-body human reaching and grasping motions using only brief walking mocap data. Base on the observation that walking data captures valuable movement patterns transferable across tasks and, on the other hand, the advanced kinematic methods can generate diverse grasping poses, which can then be interpolated into motions to serve as task-specific guidance. Our approach incorporates an active data generation strategy to maximize the utility of the generated motions, along with a local feature alignment mechanism that transfers natural movement patterns from walking data to enhance both the success rate and naturalness of the synthesized motions. By combining the fidelity and stability of natural walking with the flexibility and generalizability of task-specific generated data, our method demonstrates strong performance and robust adaptability in diverse scenes and with unseen objects.
|
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] |
549 |
EmoEdit: Evoking Emotions through Image Manipulation
|
[
"Jingyuan Yang",
"Jiawei Feng",
"Weibin Luo",
"Dani Lischinski",
"Daniel Cohen-Or",
"Hui Huang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_EmoEdit_Evoking_Emotions_through_Image_Manipulation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_EmoEdit_Evoking_Emotions_through_Image_Manipulation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_EmoEdit_Evoking_Emotions_CVPR_2025_supplemental.pdf
|
2405.12661
|
@InProceedings{Yang_2025_CVPR,
author = {Yang, Jingyuan and Feng, Jiawei and Luo, Weibin and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui},
title = {EmoEdit: Evoking Emotions through Image Manipulation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24690-24699}
}
|
Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotions. This task is inherently complex due to its twofold objective: evoking the intended emotion while preserving image composition. Existing AIM methods primarily adjust color and style, often failing to elicit precise, profound emotional shifts. Drawing on psychological insights, we introduce EmoEdit, which extends AIM by incorporating content modifications to enhance emotional impact. Specifically, we construct EmoEditSet, a large-scale AIM dataset of 40,120 paired data through emotion attribution and data construction. To make generative models emotion-aware, we design an Emotion Adapter and train it using EmoEditSet. We further propose an instruction loss to capture semantic variations in each data pair. Our method is evaluated both qualitatively and quantitatively, demonstrating superior performance over state-of-the-art techniques. Additionally, we showcase the portability of our Emotion Adapter to other diffusion-based models, enhancing their emotion knowledge with diverse semantics. Code is available at: https://github.com/JingyuanYY/EmoEdit.
|
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] |
550 |
RORem: Training a Robust Object Remover with Human-in-the-Loop
|
[
"Ruibin Li",
"Tao Yang",
"Song Guo",
"Lei Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_RORem_Training_a_Robust_Object_Remover_with_Human-in-the-Loop_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_RORem_Training_a_Robust_Object_Remover_with_Human-in-the-Loop_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_RORem_Training_a_CVPR_2025_supplemental.pdf
|
2501.00740
|
@InProceedings{Li_2025_CVPR,
author = {Li, Ruibin and Yang, Tao and Guo, Song and Zhang, Lei},
title = {RORem: Training a Robust Object Remover with Human-in-the-Loop},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14024-14035}
}
|
Despite the significant advancements, existing object removal methods struggle with incomplete removal, incorrect content synthesis and blurry synthesized regions, resulting in low success rates. Such issues are mainly caused by the lack of high-quality paired training data, as well as the self-supervised training paradigm adopted in these methods, which forces the model to in-paint the masked regions, leading to ambiguity between synthesizing the masked objects and restoring the background. To address these issues, we propose a semi-supervised learning strategy with human-in-the-loop to create high-quality paired training data, aiming to train a Robust Object Remover (RORem). We first collect 60K training pairs from open-source datasets to train an initial object removal model for generating removal samples, and then utilize human feedback to select a set of high-quality object removal pairs, with which we train a discriminator to automate the following training data generation process. By iterating this process for several rounds, we finally obtain a substantial object removal dataset with over 200K pairs. Fine-tuning the pre-trained stable diffusion model with this dataset, we obtain our RORem, which demonstrates state-of-the-art object removal performance in terms of both reliability and image quality. Particularly, RORem improves the object removal success rate over previous methods by more than 18%. The dataset, source code and trained model will be released.
|
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] |
551 |
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
|
[
"Ashmal Vayani",
"Dinura Dissanayake",
"Hasindri Watawana",
"Noor Ahsan",
"Nevasini Sasikumar",
"Omkar Thawakar",
"Henok Biadglign Ademtew",
"Yahya Hmaiti",
"Amandeep Kumar",
"Kartik Kukreja",
"Mykola Maslych",
"Wafa Al Ghallabi",
"Mihail Minkov Mihaylov",
"Chao Qin",
"Abdelrahman M. Shaker",
"Mike Zhang",
"Mahardika Krisna Ihsani",
"Amiel Gian Esplana",
"Monil Gokani",
"Shachar Mirkin",
"Harsh Singh",
"Ashay Srivastava",
"Endre Hamerlik",
"Fathinah Asma Izzati",
"Fadillah Adamsyah Maani",
"Sebastian Cavada",
"Jenny Chim",
"Rohit Gupta",
"Sanjay Manjunath",
"Kamila Zhumakhanova",
"Feno Heriniaina Rabevohitra",
"Azril Hafizi Amirudin",
"Muhammad Ridzuan",
"Daniya Najiha Abdul Kareem",
"Ketan Pravin More",
"Kunyang Li",
"Pramesh Shakya",
"Muhammad Saad",
"Amirpouya Ghasemaghaei",
"Amirbek Djanibekov",
"Dilshod Azizov",
"Branislava Jankovic",
"Naman Bhatia",
"Alvaro Cabrera",
"Johan Obando-Ceron",
"Olympiah Otieno",
"Febian Farestam",
"Muztoba Rabbani",
"Sanoojan Ballah",
"Santosh Sanjeev",
"Abduragim Shtanchaev",
"Maheen Fatima",
"Thao Nguyen",
"Amrin Kareem",
"Toluwani Aremu",
"Nathan Augusto Zacarias Xavier",
"Amit Bhatkal",
"Hawau Olamide Toyin",
"Aman Chadha",
"Hisham Cholakkal",
"Rao Muhammad Anwer",
"Michael Felsberg",
"Jorma Laaksonen",
"Thamar Solorio",
"Monojit Choudhury",
"Ivan Laptev",
"Mubarak Shah",
"Salman Khan",
"Fahad Shahbaz Khan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Vayani_All_Languages_Matter_Evaluating_LMMs_on_Culturally_Diverse_100_Languages_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Vayani_All_Languages_Matter_Evaluating_LMMs_on_Culturally_Diverse_100_Languages_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vayani_All_Languages_Matter_CVPR_2025_supplemental.pdf
|
2411.16508
|
@InProceedings{Vayani_2025_CVPR,
author = {Vayani, Ashmal and Dissanayake, Dinura and Watawana, Hasindri and Ahsan, Noor and Sasikumar, Nevasini and Thawakar, Omkar and Ademtew, Henok Biadglign and Hmaiti, Yahya and Kumar, Amandeep and Kukreja, Kartik and Maslych, Mykola and Al Ghallabi, Wafa and Mihaylov, Mihail Minkov and Qin, Chao and Shaker, Abdelrahman M. and Zhang, Mike and Ihsani, Mahardika Krisna and Esplana, Amiel Gian and Gokani, Monil and Mirkin, Shachar and Singh, Harsh and Srivastava, Ashay and Hamerlik, Endre and Izzati, Fathinah Asma and Maani, Fadillah Adamsyah and Cavada, Sebastian and Chim, Jenny and Gupta, Rohit and Manjunath, Sanjay and Zhumakhanova, Kamila and Rabevohitra, Feno Heriniaina and Amirudin, Azril Hafizi and Ridzuan, Muhammad and Kareem, Daniya Najiha Abdul and More, Ketan Pravin and Li, Kunyang and Shakya, Pramesh and Saad, Muhammad and Ghasemaghaei, Amirpouya and Djanibekov, Amirbek and Azizov, Dilshod and Jankovic, Branislava and Bhatia, Naman and Cabrera, Alvaro and Obando-Ceron, Johan and Otieno, Olympiah and Farestam, Febian and Rabbani, Muztoba and Ballah, Sanoojan and Sanjeev, Santosh and Shtanchaev, Abduragim and Fatima, Maheen and Nguyen, Thao and Kareem, Amrin and Aremu, Toluwani and Xavier, Nathan Augusto Zacarias and Bhatkal, Amit and Toyin, Hawau Olamide and Chadha, Aman and Cholakkal, Hisham and Anwer, Rao Muhammad and Felsberg, Michael and Laaksonen, Jorma and Solorio, Thamar and Choudhury, Monojit and Laptev, Ivan and Shah, Mubarak and Khan, Salman and Khan, Fahad Shahbaz},
title = {All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19565-19575}
}
|
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in multimodal research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including True/False, multiple choice, and open-ended questions, which are further divided into short and long-answer categories. ALM-bench design ensures a comprehensive assessment of a model's ability to handle varied levels of difficulty in visual and linguistic reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations. Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark will be publicly released.
|
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] |
552 |
SparseAlign: a Fully Sparse Framework for Cooperative Object Detection
|
[
"Yunshuang Yuan",
"Yan Xia",
"Daniel Cremers",
"Monika Sester"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_SparseAlign_a_Fully_Sparse_Framework_for_Cooperative_Object_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_SparseAlign_a_Fully_Sparse_Framework_for_Cooperative_Object_Detection_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_SparseAlign_a_Fully_CVPR_2025_supplemental.pdf
|
2503.12982
|
@InProceedings{Yuan_2025_CVPR,
author = {Yuan, Yunshuang and Xia, Yan and Cremers, Daniel and Sester, Monika},
title = {SparseAlign: a Fully Sparse Framework for Cooperative Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22296-22305}
}
|
Cooperative perception can increase the view field and decrease the occlusion of an ego vehicle, hence improving the perception performance and safety of autonomous driving. Despite the success of previous works on cooperative object detection, they mostly operate on dense Bird's Eye View (BEV) feature maps, which are computationally demanding and can hardly be extended to long-range detection problems. More efficient fully sparse frameworks are rarely explored. In this work, we design a fully sparse framework, SparseAlign, with three key features: an enhanced sparse 3D backbone, a query-based temporal context learning module, and a robust detection head specially tailored for sparse features. Extensive experimental results on both OPV2V and DairV2X datasets show that our framework, despite its sparsity, outperforms the state of the art with less communication bandwidth requirements. In addition, experiments on the OPV2Vt and DairV2Xt datasets for time-aligned cooperative object detection also show a significant performance gain compared to the baseline works.
|
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] |
553 |
Video-Bench: Human-Aligned Video Generation Benchmark
|
[
"Hui Han",
"Siyuan Li",
"Jiaqi Chen",
"Yiwen Yuan",
"Yuling Wu",
"Yufan Deng",
"Chak Tou Leong",
"Hanwen Du",
"Junchen Fu",
"Youhua Li",
"Jie Zhang",
"Chi Zhang",
"Li-jia Li",
"Yongxin Ni"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Video-Bench_Human-Aligned_Video_Generation_Benchmark_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Video-Bench_Human-Aligned_Video_Generation_Benchmark_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Video-Bench_Human-Aligned_Video_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Han_2025_CVPR,
author = {Han, Hui and Li, Siyuan and Chen, Jiaqi and Yuan, Yiwen and Wu, Yuling and Deng, Yufan and Leong, Chak Tou and Du, Hanwen and Fu, Junchen and Li, Youhua and Zhang, Jie and Zhang, Chi and Li, Li-jia and Ni, Yongxin},
title = {Video-Bench: Human-Aligned Video Generation Benchmark},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18858-18868}
}
|
Video generation assessment is essential for ensuring that generative models produce visually realistic, high-quality videos while aligning with human expectations. Current video generation benchmarks fall into two main categories: traditional benchmarks, which use metrics and embeddings to evaluate generated video quality across multiple dimensions but often lack alignment with human judgments; and large language model (LLM)-based benchmarks, though capable of human-like reasoning, are constrained by a limited understanding of video quality metrics and cross-modal consistency.To address these challenges and establish a benchmark that better aligns with human preferences, this paper introduces Video-Bench, a comprehensive benchmark featuring a rich prompt suite and extensive evaluation dimensions. This benchmark represents the first attempt to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models. By incorporating few-shot scoring and chain-of-query techniques, Video-Bench provides a structured, scalable approach to generated video evaluation. Experimental results demonstrate that MLLMs achieve superior alignment with human preferences across all dimensions. Moreover, in instances where our framework's assessments diverge from human evaluations, it consistently offers more objective and accurate insights, suggesting an even greater potential advantage over traditional human judgment.
|
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] |
554 |
Data Distributional Properties As Inductive Bias for Systematic Generalization
|
[
"Felipe del Rio",
"Alain Raymond-Saez",
"Daniel Florea",
"Rodrigo Toro Icarte",
"Julio Hurtado",
"Cristian B. Calderon",
"Alvaro Soto"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/del_Rio_Data_Distributional_Properties_As_Inductive_Bias_for_Systematic_Generalization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/del_Rio_Data_Distributional_Properties_As_Inductive_Bias_for_Systematic_Generalization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/del_Rio_Data_Distributional_Properties_CVPR_2025_supplemental.pdf
|
2502.20499
|
@InProceedings{del_Rio_2025_CVPR,
author = {del Rio, Felipe and Raymond-Saez, Alain and Florea, Daniel and Icarte, Rodrigo Toro and Hurtado, Julio and Calderon, Cristian B. and Soto, Alvaro},
title = {Data Distributional Properties As Inductive Bias for Systematic Generalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25590-25601}
}
|
Deep neural networks (DNNs) struggle at systematic generalization (SG). Several studies have evaluated the possibility of promoting SG through the proposal of novel architectures, loss functions, or training methodologies. Few studies, however, have focused on the role of training data properties in promoting SG. In this work, we investigate the impact of certain data distributional properties, as inductive biases for the SG ability of a multi-modal language model. To this end, we study three different properties. First, data diversity, instantiated as an increase in the possible values a latent property in the training distribution may take. Second, burstiness, where we probabilistically restrict the number of possible values of latent factors on particular inputs during training. Third, latent intervention, where a particular latent factor is altered randomly during training. We find that all three factors significantly enhance SG, with diversity contributing an 89% absolute increase in accuracy in the most affected property. Through a series of experiments, we test various hypotheses to understand why these properties promote SG. Finally, we find that Normalized Mutual Information (NMI) between latent attributes in the training distribution is strongly predictive of out-of-distribution generalization. We find that a mechanism by which lower NMI induces SG is in the geometry of representations. In particular, we find that NMI induces more parallelism in neural representations (i.e., input features coded in parallel neural vectors) of the model, a property related to the capacity of reasoning by analogy.
|
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] |
555 |
MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization
|
[
"Siyuan Li",
"Luyuan Zhang",
"Zedong Wang",
"Juanxi Tian",
"Cheng Tan",
"Zicheng Liu",
"Chang Yu",
"Qingsong Xie",
"Haonan Lu",
"Haoqian Wang",
"Zhen Lei"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MergeVQ_A_Unified_Framework_for_Visual_Generation_and_Representation_with_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MergeVQ_A_Unified_Framework_for_Visual_Generation_and_Representation_with_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MergeVQ_A_Unified_CVPR_2025_supplemental.pdf
|
2504.00999
|
@InProceedings{Li_2025_CVPR,
author = {Li, Siyuan and Zhang, Luyuan and Wang, Zedong and Tian, Juanxi and Tan, Cheng and Liu, Zicheng and Yu, Chang and Xie, Qingsong and Lu, Haonan and Wang, Haoqian and Lei, Zhen},
title = {MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19713-19723}
}
|
Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great success in both self-supervised pre-training and image generation. However, most existing methods struggle to address the trade-off in the shared latent space for generation quality vs. representation learning and efficiency. To push the limits of this paradigm, we propose MergeVQ, which incorporates token merging techniques into VQ-based generative models to bridge the gap between image generation and visual representation learning in a unified architecture. During pre-training, MergeVQ decouples top-k semantics from latent space with the token merge module after self-attention blocks in the encoder for subsequent Look-up Free Quantization (LFQ) and global alignment and recovers their fine-grained details through cross-attention in the decoder for reconstruction. As for second-stage generation, we introduce MergeAR, which performs KV Cache compression for efficient raster-order prediction. Extensive experiments on ImageNet verify that MergeVQ as an AR generative model achieves competitive performance in both visual representation learning and image generation tasks while maintaining favorable token efficiency and inference speed. The code and model will be available at https://apexgen-x.github.io/MergeVQ.
|
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] |
556 |
InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation
|
[
"Sirui Xu",
"Dongting Li",
"Yucheng Zhang",
"Xiyan Xu",
"Qi Long",
"Ziyin Wang",
"Yunzhi Lu",
"Shuchang Dong",
"Hezi Jiang",
"Akshat Gupta",
"Yu-Xiong Wang",
"Liang-Yan Gui"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_InterAct_Advancing_Large-Scale_Versatile_3D_Human-Object_Interaction_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_InterAct_Advancing_Large-Scale_Versatile_3D_Human-Object_Interaction_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_InterAct_Advancing_Large-Scale_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Xu_2025_CVPR,
author = {Xu, Sirui and Li, Dongting and Zhang, Yucheng and Xu, Xiyan and Long, Qi and Wang, Ziyin and Lu, Yunzhi and Dong, Shuchang and Jiang, Hezi and Gupta, Akshat and Wang, Yu-Xiong and Gui, Liang-Yan},
title = {InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7048-7060}
}
|
While large-scale human motion capture datasets have advanced human motion generation, modeling and generating dynamic 3D human-object interactions (HOIs) remain challenging due to dataset limitations. Existing datasets often lack extensive, high-quality motion and annotation and exhibit artifacts such as contact penetration, floating, and incorrect hand motions. To address these issues, we introduce InterAct, a large-scale 3D HOI benchmark featuring dataset and methodological advancements. First, we consolidate and standardize 21.81 hours of HOI data from diverse sources, enriching it with detailed textual annotations. Second, we propose a unified optimization framework to enhance data quality by reducing artifacts and correcting hand motions. Leveraging the principle of contact invariance, we maintain human-object relationships while introducing motion variations, expanding the dataset to 30.70 hours. Third, we define six benchmarking tasks and develop a unified HOI generative modeling perspective, achieving state-of-the-art performance. Extensive experiments validate the utility of our dataset as a foundational resource for advancing 3D human-object interaction generation. The dataset will be publicly accessible to support further research in the field.
|
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] |
557 |
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
|
[
"Meilong Xu",
"Saumya Gupta",
"Xiaoling Hu",
"Chen Li",
"Shahira Abousamra",
"Dimitris Samaras",
"Prateek Prasanna",
"Chao Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_TopoCellGen_Generating_Histopathology_Cell_Topology_with_a_Diffusion_Model_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_TopoCellGen_Generating_Histopathology_Cell_Topology_with_a_Diffusion_Model_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_TopoCellGen_Generating_Histopathology_CVPR_2025_supplemental.pdf
|
2412.06011
|
@InProceedings{Xu_2025_CVPR,
author = {Xu, Meilong and Gupta, Saumya and Hu, Xiaoling and Li, Chen and Abousamra, Shahira and Samaras, Dimitris and Prasanna, Prateek and Chen, Chao},
title = {TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20979-20989}
}
|
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting training data, aligns more closely with pathologists' domain knowledge, and offers new opportunities for controlling and generalizing the tumor microenvironment. In this paper, we propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies. Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks such as cell detection and classification. To assess the topological fidelity of generated layouts, we introduce a new metric, Topological Frechet Distance (TopoFD), which overcomes the limitations of traditional metrics like FID in evaluating topological structure. Experimental results demonstrate the effectiveness of our approach in generating multi-class cell layouts that capture intricate topological relationships. Code is available at https://github.com/Melon-Xu/TopoCellGen.
|
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558 |
Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models
|
[
"Jiaming Zhang",
"Junhong Ye",
"Xingjun Ma",
"Yige Li",
"Yunfan Yang",
"Yunhao Chen",
"Jitao Sang",
"Dit-Yan Yeung"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Anyattack_Towards_Large-scale_Self-supervised_Adversarial_Attacks_on_Vision-language_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Anyattack_Towards_Large-scale_Self-supervised_Adversarial_Attacks_on_Vision-language_Models_CVPR_2025_paper.pdf
| null |
2410.05346
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Jiaming and Ye, Junhong and Ma, Xingjun and Li, Yige and Yang, Yunfan and Chen, Yunhao and Sang, Jitao and Yeung, Dit-Yan},
title = {Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19900-19909}
}
|
Due to their multimodal capabilities, Vision-Language Models (VLMs) have found numerous impactful applications in real-world scenarios. However, recent studies have revealed that VLMs are vulnerable to image-based adversarial attacks. Traditional targeted adversarial attacks require specific targets and labels, limiting their real-world impact. We present AnyAttack, a self-supervised framework that transcends the limitations of conventional attacks through a novel foundation model approach. By pre-training on the massive LAION-400M dataset without label supervision, AnyAttack achieves unprecedented flexibility - enabling any image to be transformed into an attack vector targeting any desired output across different VLMs. This approach fundamentally changes the threat landscape, making adversarial capabilities accessible at an unprecedented scale. Our extensive validation across five open-source VLMs (CLIP, BLIP, BLIP2, InstructBLIP, and MiniGPT-4) demonstrates AnyAttack's effectiveness across diverse multimodal tasks. Most concerning, AnyAttack seamlessly transfers to commercial systems including Google Gemini, Claude Sonnet, Microsoft Copilot and OpenAI GPT, revealing a systemic vulnerability requiring immediate attention.
|
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] |
559 |
Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video
|
[
"Hoang Chuong Nguyen",
"Wei Mao",
"Jose M. Alvarez",
"Miaomiao Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_Joint_Optimization_of_Neural_Radiance_Fields_and_Continuous_Camera_Motion_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_Joint_Optimization_of_Neural_Radiance_Fields_and_Continuous_Camera_Motion_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_Joint_Optimization_of_CVPR_2025_supplemental.zip
|
2504.19819
|
@InProceedings{Nguyen_2025_CVPR,
author = {Nguyen, Hoang Chuong and Mao, Wei and Alvarez, Jose M. and Liu, Miaomiao},
title = {Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11472-11481}
}
|
Neural Radiance Fields (NeRF) has demonstrated its superior capability to represent 3D geometry but require accurately precomputed camera poses during training. To mitigate this requirement, existing methods jointly optimize camera poses and NeRF often relying on good pose initialisation or depth priors. However, these approaches struggle in challenging scenarios, such as large rotations, as they map each camera to a world coordinate system. We propose a novel method that eliminates prior dependencies by modeling continuous camera motions as time-dependent angular velocity and velocity. Relative motions between cameras are learned first via velocity integration, while camera poses can be obtained by aggregating such relative motions up to a world coordinate system defined at a single time step within the video. Specifically, accurate continuous camera movements are learned through a time-dependent NeRF, which captures local scene geometry and motion by training from neighboring frames for each time step. The learned motions enable fine-tuning the NeRF to represent the full scene geometry. Experiments on Co3D and Scannet show our approach achieves superior camera pose and depth estimation and comparable novel-view synthesis performance compared to state-of-the-art methods.
|
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] |
560 |
IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing
|
[
"Chun Gu",
"Xiaofei Wei",
"Zixuan Zeng",
"Yuxuan Yao",
"Li Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_IRGS_Inter-Reflective_Gaussian_Splatting_with_2D_Gaussian_Ray_Tracing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_IRGS_Inter-Reflective_Gaussian_Splatting_with_2D_Gaussian_Ray_Tracing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_IRGS_Inter-Reflective_Gaussian_CVPR_2025_supplemental.zip
|
2412.15867
|
@InProceedings{Gu_2025_CVPR,
author = {Gu, Chun and Wei, Xiaofei and Zeng, Zixuan and Yao, Yuxuan and Zhang, Li},
title = {IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10943-10952}
}
|
In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate incident light, resulting in inaccurate material and lighting estimations. To this end, we introduce the inter-reflective Gaussian splatting (IRGS) framework for inverse rendering. To capture inter-reflection, we apply the full rendering equation without simplification and compute incident radiance on the fly using the proposed differentiable 2D Gaussian ray tracing. Additionally, we present an efficient optimization scheme to handle the computational demands of Monte Carlo sampling for rendering equation evaluation. Furthermore, we introduce a novel strategy for querying the indirect radiance of incident light when relighting the optimized scenes. Extensive experiments on multiple standard benchmarks validate the effectiveness of IRGS, demonstrating its capability to accurately model complex inter-reflection effects.
|
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] |
561 |
InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions
|
[
"Sirui Xu",
"Hung Yu Ling",
"Yu-Xiong Wang",
"Liang-Yan Gui"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_InterMimic_Towards_Universal_Whole-Body_Control_for_Physics-Based_Human-Object_Interactions_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_InterMimic_Towards_Universal_Whole-Body_Control_for_Physics-Based_Human-Object_Interactions_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_InterMimic_Towards_Universal_CVPR_2025_supplemental.zip
|
2502.20390
|
@InProceedings{Xu_2025_CVPR,
author = {Xu, Sirui and Ling, Hung Yu and Wang, Yu-Xiong and Gui, Liang-Yan},
title = {InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12266-12277}
}
|
Achieving realistic simulations of humans interacting with a wide range of objects has long been a fundamental goal. Extending physics-based motion imitation to complex human-object interactions (HOIs) is challenging due to intricate human-object coupling, variability in object geometries, and artifacts in motion capture data, such as inaccurate contacts and limited hand detail. We introduce InterMimic, a framework that enables a single policy to robustly learn from hours of imperfect MoCap data covering diverse full-body interactions with dynamic and varied objects. Our key insight is to employ a curriculum strategy -- perfect first, then scale up. We first train subject-specific teacher policies to mimic, retarget, and refine motion capture data. Next, we distill these teachers into a student policy, with the teachers acting as online experts providing direct supervision, as well as high-quality references. Notably, we incorporate RL fine-tuning on the student policy to surpass mere demonstration replication and achieve higher-quality solutions. Our experiments demonstrate that InterMimic produces realistic and diverse interactions across multiple HOI datasets. The learned policy generalizes in a zero-shot manner and seamlessly integrates with kinematic generators, elevating the framework from mere imitation to generative modeling of complex human-object interactions.
|
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] |
562 |
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
|
[
"Zichen Tian",
"Yaoyao Liu",
"Qianru Sun"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Meta-Learning_Hyperparameters_for_Parameter_Efficient_Fine-Tuning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Meta-Learning_Hyperparameters_for_Parameter_Efficient_Fine-Tuning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Meta-Learning_Hyperparameters_for_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Tian_2025_CVPR,
author = {Tian, Zichen and Liu, Yaoyao and Sun, Qianru},
title = {Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23037-23047}
}
|
Training large foundation models from scratch for domain-specific applications is almost impossible due to data limits and long-tailed distributions -- taking remote sensing (RS) as an example. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module influence during fine-tuning. MetaPEFT dynamically adjusts three key factors of PEFT on RS images: module insertion, layer selection, and module-wise learning rates, which collectively control the influence of PEFT modules across the network. We conduct extensive experiments on three transfer-learning scenarios and five datasets in both RS and natural image domains. The results show that MetaPEFT achieves state-of-the-art performance in cross-spectral adaptation, requiring only a small amount of trainable parameters and improving tail-class accuracy significantly.
|
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] |
563 |
TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
|
[
"Dana Cohen-Bar",
"Daniel Cohen-Or",
"Gal Chechik",
"Yoni Kasten"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cohen-Bar_TriTex_Learning_Texture_from_a_Single_Mesh_via_Triplane_Semantic_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cohen-Bar_TriTex_Learning_Texture_from_a_Single_Mesh_via_Triplane_Semantic_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cohen-Bar_TriTex_Learning_Texture_CVPR_2025_supplemental.pdf
|
2503.16630
|
@InProceedings{Cohen-Bar_2025_CVPR,
author = {Cohen-Bar, Dana and Cohen-Or, Daniel and Chechik, Gal and Kasten, Yoni},
title = {TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21403-21413}
}
|
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-to-image diffusion models for texturing, they often struggle to preserve the appearance of the source texture during texture transfer. We present TriTex, a novel approach that learns a volumetric texture field from a single textured mesh by mapping semantic features to surface colors. Using an efficient triplane-based architecture, our method enables semantic-aware texture transfer to a novel target mesh. Despite training on just one example, it generalizes effectively to diverse shapes within the same category. Extensive evaluation on our newly created benchmark dataset shows that TriTex achieves superior texture transfer quality and fast inference times compared to existing methods. Our approach advances single-example texture transfer, providing a practical solution for maintaining visual coherence across related 3D models in applications like game development and simulation.
|
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0.006553213112056255,
0.002675792668014765,
0.05013025552034378,
0.005859652534127235
] |
564 |
Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning
|
[
"Kunyu Wang",
"Xueyang Fu",
"Xin Lu",
"Chengjie Ge",
"Chengzhi Cao",
"Wei Zhai",
"Zheng-Jun Zha"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Efficient_Test-time_Adaptive_Object_Detection_via_Sensitivity-Guided_Pruning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Efficient_Test-time_Adaptive_Object_Detection_via_Sensitivity-Guided_Pruning_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Kunyu and Fu, Xueyang and Lu, Xin and Ge, Chengjie and Cao, Chengzhi and Zhai, Wei and Zha, Zheng-Jun},
title = {Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10577-10586}
}
|
Continual test-time adaptive object detection (CTTA-OD) aims to online adapt a source pre-trained detector to ever-changing environments during inference under continuous domain shifts. Most existing CTTA-OD methods prioritize effectiveness while overlooking computational efficiency, which is crucial for resource-constrained scenarios. In this paper, we propose an efficient CTTA-OD method via pruning. Our motivation stems from the observation that not all learned source features are beneficial; certain domain-sensitive feature channels can adversely affect target domain performance. Inspired by this, we introduce a sensitivity-guided channel pruning strategy that quantifies each channel based on its sensitivity to domain discrepancies at both image and instance levels. We apply weighted sparsity regularization to selectively suppress and prune these sensitive channels, focusing adaptation efforts on invariant ones. Additionally, we introduce a stochastic channel reactivation mechanism to restore pruned channels, enabling recovery of potentially useful features and mitigating the risks of early pruning. Extensive experiments on three benchmarks show that our method achieves superior adaptation performance while reducing computational overhead by 12% in FLOPs compared to the recent SOTA method.
|
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] |
565 |
A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning
|
[
"Xin Wen",
"Bingchen Zhao",
"Yilun Chen",
"Jiangmiao Pang",
"Xiaojuan Qi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_A_Data-Centric_Revisit_of_Pre-Trained_Vision_Models_for_Robot_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_A_Data-Centric_Revisit_of_Pre-Trained_Vision_Models_for_Robot_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wen_A_Data-Centric_Revisit_CVPR_2025_supplemental.pdf
|
2503.06960
|
@InProceedings{Wen_2025_CVPR,
author = {Wen, Xin and Zhao, Bingchen and Chen, Yilun and Pang, Jiangmiao and Qi, Xiaojuan},
title = {A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12143-12154}
}
|
Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. Through systematic evaluation, we find that while DINO and iBOT outperform MAE across visuomotor control and perception tasks, they struggle when trained on non-(single-)object-centric (NOC) data--a limitation strongly correlated with their diminished ability to learn object-centric representations. This investigation indicates that the ability to form object-centric representations from the non-object-centric robotics dataset is the key to success for PVMs. Motivated by this discovery, we designed SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck to reduce the number of prototypes to encourage the emergence of objectness as well as cross-view consistency regularization for encouraging multiview invariance. Our experiments encompass pre-training on object-centric, scene-centric, web-crawled, and ego-centric data. Across all settings, our approach learns transferrable representations and achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations. When scaled up with million-scale datasets, our method also demonstrates superior data efficiency and scalability. Our code and models are publicly available at https://github.com/CVMI-Lab/SlotMIM.
|
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] |
566 |
Visual Agentic AI for Spatial Reasoning with a Dynamic API
|
[
"Damiano Marsili",
"Rohun Agrawal",
"Yisong Yue",
"Georgia Gkioxari"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Marsili_Visual_Agentic_AI_for_Spatial_Reasoning_with_a_Dynamic_API_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Marsili_Visual_Agentic_AI_for_Spatial_Reasoning_with_a_Dynamic_API_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Marsili_Visual_Agentic_AI_CVPR_2025_supplemental.pdf
|
2502.06787
|
@InProceedings{Marsili_2025_CVPR,
author = {Marsili, Damiano and Agrawal, Rohun and Yue, Yisong and Gkioxari, Georgia},
title = {Visual Agentic AI for Spatial Reasoning with a Dynamic API},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19446-19455}
}
|
Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progress in AI has led to vision and language models capable of answering questions from images. However, their performance declines when tasked with 3D spatial reasoning. To tackle the complexity of such reasoning problems, we introduce an agentic program synthesis approach where LLM agents collaboratively generate a Pythonic API with new functions to solve common subproblems. Our method overcomes limitations of prior approaches that rely on a static, human-defined API, allowing it to handle a wider range of queries. To assess AI capabilities for 3D understanding, we introduce a new benchmark of queries involving multiple steps of grounding and inference. We show that our method outperforms prior zero-shot models for visual reasoning in 3D and empirically validate the effectiveness of our agentic framework for 3D spatial reasoning tasks. Project website: https://glab-caltech.github.io/vadar/
|
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] |
567 |
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
|
[
"Yilong Wang",
"Zilin Gao",
"Qilong Wang",
"Zhaofeng Chen",
"Peihua Li",
"Qinghua Hu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TAMT_Temporal-Aware_Model_Tuning_for_Cross-Domain_Few-Shot_Action_Recognition_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TAMT_Temporal-Aware_Model_Tuning_for_Cross-Domain_Few-Shot_Action_Recognition_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TAMT_Temporal-Aware_Model_CVPR_2025_supplemental.pdf
|
2411.19041
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Yilong and Gao, Zilin and Wang, Qilong and Chen, Zhaofeng and Li, Peihua and Hu, Qinghua},
title = {TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3449-3459}
}
|
Going beyond few-shot action recognition (FSAR), cross-domain FSAR (CDFSAR) has attracted recent research interests by solving the domain gap lying in source-to-target transfer learning. Existing CDFSAR methods mainly focus on joint training of source and target data to mitigate the side effect of domain gap. However, such kind of methods suffer from two limitations: First, pair-wise joint training requires retraining deep models in case of one source data and multiple target ones, which incurs heavy computation cost, especially for large source and small target data. Second, pre-trained models after joint training are adopted to target domain in a straightforward manner, hardly taking full potential of pre-trained models and then limiting recognition performance. To overcome above limitations, this paper proposes a simple yet effective baseline, namely Temporal-Aware Model Tuning (TAMT) for CDFSAR. Specifically, our TAMT involves a decoupled paradigm by performing pre-training on source data and fine-tuning target data, which avoids retraining for multiple target data with single source. To effectively and efficiently explore the potential of pre-trained models in transferring to target domain, our TAMT proposes a Hierarchical Temporal Tuning Network (HTTN), whose core involves local temporal-aware adapters (TAA) and a global temporal-aware moment tuning (GTMT). Particularly, TAA learns few parameters to recalibrate the intermediate features of frozen pre-trained models, enabling efficient adaptation to target domains. Furthermore, GTMT helps to generate powerful video representations, improving match performance on the target domain. Experiments on several widely used video benchmarks show our TAMT outperforms the recently proposed counterparts by 13% 31%, achieving new state-of-the-art CDFSAR results.
|
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] |
568 |
Feature Spectrum Learning for Remote Sensing Change Detection
|
[
"Qi Zang",
"Dong Zhao",
"Shuang Wang",
"Dou Quan",
"Zhun Zhong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zang_Feature_Spectrum_Learning_for_Remote_Sensing_Change_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zang_Feature_Spectrum_Learning_for_Remote_Sensing_Change_Detection_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zang_Feature_Spectrum_Learning_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zang_2025_CVPR,
author = {Zang, Qi and Zhao, Dong and Wang, Shuang and Quan, Dou and Zhong, Zhun},
title = {Feature Spectrum Learning for Remote Sensing Change Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12647-12657}
}
|
Change detection (CD) holds significant implications for Earth observation, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challenges. Existing methods mainly regard pseudo-changes as a kind of style shift and alleviate it by transforming bitemporal images into the same style using generative adversarial networks (GANs). Nevertheless, their efforts are limited by the complexity of optimizing GANs and the absence of guidance from physical properties. This paper finds that the spectrum transformation (ST) has the potential to mitigate pseudo-changes by aligning in the frequency domain carrying the style. However, the benefit of ST is largely constrained by two drawbacks: 1) limited transformation space and 2) inefficient parameter search. To address these limitations, we propose a Feature Spectrum learning (FeaSpect) that adaptively eliminate pseudo-changes in the latent space. For the drawback 1), FeaSpect directs the transformation towards style-aligned discriminative features via feature spectrum transformation (FST). For the drawback 2), FeaSpect allows FST to be trainable, efficiently discovering optimal parameters via extraction box with adaptive attention and extraction box with learnable strides. Extensive experiments on challenging datasets demonstrate that our method remarkably outperforms existing methods and achieves a commendable trade-off between accuracy and efficiency. Importantly, our method can be easily injected into other frameworks, achieving consistent improvements.
|
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] |
569 |
BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
|
[
"Amaya Gallagher-Syed",
"Henry Senior",
"Omnia Alwazzan",
"Elena Pontarini",
"Michele Bombardieri",
"Costantino Pitzalis",
"Myles J. Lewis",
"Michael R. Barnes",
"Luca Rossi",
"Gregory Slabaugh"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_Diagnostics_for_Multistain_IHC_Computational_Pathology_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_Diagnostics_for_Multistain_IHC_Computational_Pathology_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gallagher-Syed_BioX-CPath_Biologically-driven_Explainable_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Gallagher-Syed_2025_CVPR,
author = {Gallagher-Syed, Amaya and Senior, Henry and Alwazzan, Omnia and Pontarini, Elena and Bombardieri, Michele and Pitzalis, Costantino and Lewis, Myles J. and Barnes, Michael R. and Rossi, Luca and Slabaugh, Gregory},
title = {BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10372-10383}
}
|
The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification that leverages both spatial and semantic features across multiple stains. At its core, BioXCPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis and Sjogren's Disease multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores, that permit measuring model alignment with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Source code and documentation can be found at: https://github.com/AmayaGS/BioX-CPath.
|
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] |
570 |
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
|
[
"Guosheng Zhao",
"Chaojun Ni",
"Xiaofeng Wang",
"Zheng Zhu",
"Xueyang Zhang",
"Yida Wang",
"Guan Huang",
"Xinze Chen",
"Boyuan Wang",
"Youyi Zhang",
"Wenjun Mei",
"Xingang Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DriveDreamer4D_World_Models_Are_Effective_Data_Machines_for_4D_Driving_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DriveDreamer4D_World_Models_Are_Effective_Data_Machines_for_4D_Driving_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DriveDreamer4D_World_Models_CVPR_2025_supplemental.zip
|
2410.13571
|
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Guosheng and Ni, Chaojun and Wang, Xiaofeng and Zhu, Zheng and Zhang, Xueyang and Wang, Yida and Huang, Guan and Chen, Xinze and Wang, Boyuan and Zhang, Youyi and Mei, Wenjun and Wang, Xingang},
title = {DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12015-12026}
}
|
Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on conditions closely aligned with training data distributions, which are largely confined to forward-driving scenarios. Consequently, these methods face limitations when rendering complex maneuvers (e.g., lane change, acceleration, deceleration). Recent advancements in autonomous-driving world models have demonstrated the potential to generate diverse driving videos. However, these approaches remain constrained to 2D video generation, inherently lacking the spatiotemporal coherence required to capture intricacies of dynamic driving environments.In this paper, we introduce DriveDreamer4D, which enhances 4D driving scene representation leveraging world model priors. Specifically, we utilize the world model as a data machine to synthesize novel trajectory videos, where structured conditions are explicitly leveraged to control the spatial-temporal consistency of traffic elements. Besides, the cousin data training strategy is proposed to facilitate merging real and synthetic data for optimizing 4DGS. To our knowledge, DriveDreamer4D is the first to utilize video generation models for improving 4D reconstruction in driving scenarios.Experimental results reveal that DriveDreamer4D significantly enhances generation quality under novel trajectory views, achieving a relative improvement in FID by 32.1%, 46.4%, and 16.3% compared to PVG, S3Gaussian, and Deformable-GS. Moreover, DriveDreamer4D markedly enhances the spatiotemporal coherence of driving agents, which is verified by a comprehensive user study and the relative increases of 22.6%, 43.5%, and 15.6% in the NTA-IoU metric.
|
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] |
571 |
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models
|
[
"Xuan Cai",
"Renjie Pan",
"Hua Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_LoKi_Low-dimensional_KAN_for_Efficient_Fine-tuning_Image_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_LoKi_Low-dimensional_KAN_for_Efficient_Fine-tuning_Image_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_LoKi_Low-dimensional_KAN_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Cai_2025_CVPR,
author = {Cai, Xuan and Pan, Renjie and Yang, Hua},
title = {LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14869-14880}
}
|
'Pre-training + fine-tuning' has been widely used in various downstream tasks. Parameter-efficient fine-tuning (PEFT) has demonstrated higher efficiency and promising performance comapred to traditional full-tuning. The widely used adapter-based and prompt-based methods in PEFT can be uniformly represented as adding an MLP structure to the pre-trained model. These methods are prone to over-fitting in downstream tasks, due to the difference in data scale and distribution. To address this issue, we propose a new adapter-based PEFT module, i.e., LoKi, which consists of an encoder, a learnable activation layer, and a decoder. To maintain the simplicity of LoKi, we use single-layer linear networks for the encoder and decoder, and for the learnable activation layer, we use a Kolmogorov-Arnold Network (KAN) with the minimal number of layers (only 2 KAN linear layers). With a bottleneck rate much lower than that of Adapter, LoKi is equipped with fewer parameters (only half of Adapter) and eliminates slow training speed and high memory usage of KAN. We conduct extensive experiments on LoKi under image classification and video action recognition across 9 datasets. LoKi demonstrates highly competitive generalization performance compared to other PEFT methods with fewer tunable parameters, ensuring both effectiveness and efficiency.
|
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] |
572 |
Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration
|
[
"Kim Jun-Seong",
"GeonU Kim",
"Kim Yu-Ji",
"Yu-Chiang Frank Wang",
"Jaesung Choe",
"Tae-Hyun Oh"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jun-Seong_Dr._Splat_Directly_Referring_3D_Gaussian_Splatting_via_Direct_Language_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jun-Seong_Dr._Splat_Directly_Referring_3D_Gaussian_Splatting_via_Direct_Language_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jun-Seong_Dr._Splat_Directly_CVPR_2025_supplemental.zip
|
2502.16652
|
@InProceedings{Jun-Seong_2025_CVPR,
author = {Jun-Seong, Kim and Kim, GeonU and Yu-Ji, Kim and Wang, Yu-Chiang Frank and Choe, Jaesung and Oh, Tae-Hyun},
title = {Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14137-14146}
}
|
We introduce Dr. Splat, a novel approach for open-vocabulary 3D scene understanding leveraging 3D Gaussian Splatting. Unlike existing language-embedded 3DGS methods, which rely on a rendering process, our method directly associates language-aligned CLIP embeddings with 3D Gaussians for holistic 3D scene understanding. The key of our method is a language feature registration technique where CLIP embeddings are assigned to the dominant Gaussians intersected by each pixel-ray. Moreover, we integrate Product Quantization (PQ) trained on general large scale image data to compactly represent embeddings without per-scene optimization. Experiments demonstrate that our approach significantly outperforms existing approaches in 3D perception benchmarks, such as open-vocabulary 3D semantic segmentation, 3D object localization, and 3D object selection tasks.
|
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] |
573 |
Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection
|
[
"Feng Yan",
"Xiaoheng Jiang",
"Yang Lu",
"Jiale Cao",
"Dong Chen",
"Mingliang Xu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Wavelet_and_Prototype_Augmented_Query-based_Transformer_for_Pixel-level_Surface_Defect_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Wavelet_and_Prototype_Augmented_Query-based_Transformer_for_Pixel-level_Surface_Defect_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Wavelet_and_Prototype_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Yan_2025_CVPR,
author = {Yan, Feng and Jiang, Xiaoheng and Lu, Yang and Cao, Jiale and Chen, Dong and Xu, Mingliang},
title = {Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23860-23869}
}
|
As an important part of intelligent manufacturing, pixel-level surface defect detection (SDD) aims to locate defect areas through mask prediction. Previous methods adopt the image-independent static convolution to indiscriminately classify per-pixel features for mask prediction, which leads to suboptimal results for some challenging scenes such as weak defects and cluttered backgrounds. In this paper, inspired by query-based methods, we propose a Wavelet and Prototype Augmented Query-based Transformer (WPFormer) for surface defect detection. Specifically, a set of dynamic queries for mask prediction is updated through the dual-domain transformer decoder. Firstly, a Wavelet-enhanced Cross-Attention (WCA) is proposed, which aggregates meaningful high- and low-frequency information of image features in the wavelet domain to refine queries. WCA enhances the representation of high-frequency components by capturing multi-scale relationships between different frequency components, enabling queries to focus more on defect details. Secondly, a Prototype-guided Cross-Attention (PCA) is proposed to refine queries through meta-prototypes in the spatial domain. The prototypes aggregate semantically meaningful tokens from image features, facilitating queries to aggregate crucial defect information under the cluttered backgrounds. Extensive experiments on three defect detection datasets (i.e., ESDIs-SOD, CrackSeg9k, and ZJU-Leaper) demonstrate that the proposed method achieves state-of-the-art performance in defect detection. The code will be available at https://github.com/yfhdm/WPFormer.
|
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] |
574 |
GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection
|
[
"José Henrique Lima Marques",
"Jeffri Murrugarra-Llerena",
"Claudio R. Jung"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Marques_GauCho_Gaussian_Distributions_with_Cholesky_Decomposition_for_Oriented_Object_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Marques_GauCho_Gaussian_Distributions_with_Cholesky_Decomposition_for_Oriented_Object_Detection_CVPR_2025_paper.pdf
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https://openaccess.thecvf.com/content/CVPR2025/supplemental/Marques_GauCho_Gaussian_Distributions_CVPR_2025_supplemental.pdf
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2502.01565
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@InProceedings{Marques_2025_CVPR,
author = {Marques, Jos\'e Henrique Lima and Murrugarra-Llerena, Jeffri and Jung, Claudio R.},
title = {GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3593-3602}
}
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Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions based on Gaussian distributions has become attractive since they yield simple and differentiable terms. However, existing solutions are still based on regression heads that produce Oriented Bounding Boxes (OBBs), and the known problem of angular boundary discontinuity persists. In this work, we propose a regression head for OOD that directly produces Gaussian distributions based on the Cholesky matrix decomposition. The proposed head, named Gaucho, theoretically mitigates the boundary discontinuity problem and is fully compatible with recent Gaussian-based regression loss functions. Furthermore, we advocate using Oriented Ellipses (OEs) to represent oriented objects, which relates to GauCho through a bijective function and alleviates the encoding ambiguity problem for circular objects. Our experimental results show that GauCho can be a viable alternative to the traditional OBB head, achieving results comparable to or better than state-of-the-art detectors for the challenging dataset DOTA.
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575 |
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering
|
[
"Yuanhao Zou",
"Zhaozheng Yin"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zou_Alignment_Mining_and_Fusion_Representation_Alignment_with_Hard_Negative_Mining_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zou_Alignment_Mining_and_Fusion_Representation_Alignment_with_Hard_Negative_Mining_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zou_Alignment_Mining_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zou_2025_CVPR,
author = {Zou, Yuanhao and Yin, Zhaozheng},
title = {Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29623-29633}
}
|
Medical Visual Question Answering (Med-VQA) is a challenging task that requires a deep understanding of both medical images and textual questions. Although recent works leveraging Medical Vision-Language Pre-training (Med-VLP) have shown strong performance on the Med-VQA task, there is still no unified solution for modality alignment, and the issue of hard negatives remains under-explored. Additionally, commonly used knowledge fusion techniques for Med-VQA may introduce irrelevant information. In this work, we propose a framework to address these challenges through three key contributions: (1) a unified solution for heterogeneous modality alignments across multiple levels, modalities, views, and stages, leveraging methods such as contrastive learning and optimal transport theory; (2) a hard negative mining method that employs soft labels for multi-modality alignments and enforces the hard negative pair discrimination; and (3) a Gated Cross-Attention Module for Med-VQA that integrates the answer vocabulary as prior knowledge and select relevant information from it. Our framework outperforms the previous state-of-the-art on widely used Med-VQA datasets like RAD-VQA, SLAKE, PathVQA and VQA-2019. The code will be publicly available.
|
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] |
576 |
No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather
|
[
"Junsung Park",
"Hwijeong Lee",
"Inha Kang",
"Hyunjung Shim"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Park_No_Thing_Nothing_Highlighting_Safety-Critical_Classes_for_Robust_LiDAR_Semantic_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_No_Thing_Nothing_Highlighting_Safety-Critical_Classes_for_Robust_LiDAR_Semantic_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_No_Thing_Nothing_CVPR_2025_supplemental.pdf
|
2503.15910
|
@InProceedings{Park_2025_CVPR,
author = {Park, Junsung and Lee, Hwijeong and Kang, Inha and Shim, Hyunjung},
title = {No Thing, Nothing: Highlighting Safety-Critical Classes for Robust LiDAR Semantic Segmentation in Adverse Weather},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6690-6699}
}
|
Existing domain generalization methods for LiDAR semantic segmentation under adverse weather struggle to accurately predict "things" categories compared to "stuff" categories. In typical driving scenes, "things" categories can be dynamic and associated with higher collision risks, making them crucial for safe navigation and planning. Recognizing the importance of "things" categories, we identify their performance drop as a serious bottleneck in existing approaches. We observed that adverse weather induces degradation of semantic-level features and both corruption of local features, leading to a misprediction of "things" as "stuff". To mitigate these corruptions, we suggests our method, NTN - segmeNt Things for No-accident. To address semantic-level feature corruption, we bind each point feature to its superclass, preventing the misprediction of things classes into visually dissimilar categories. Additionally, to enhance robustness against local corruption caused by adverse weather, we define each LiDAR beam as a local region and propose a regularization term that aligns the clean data with its corrupted counterpart in feature space. NTN achieves state-of-the-art performance with a +2.6 mIoU gain on the SemanticKITTI-to-SemanticSTF benchmark and +7.9 mIoU on the SemanticPOSS-to-SemanticSTF benchmark. Notably, NTN achieves a +4.8 and +7.9 mIoU improvement on "things" classes, respectively, highlighting its effectiveness.
|
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] |
577 |
Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis
|
[
"Jeonghwan Park",
"Niall McLaughlin",
"Ihsen Alouani"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Mind_the_Gap_Detecting_Black-box_Adversarial_Attacks_in_the_Making_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Mind_the_Gap_Detecting_Black-box_Adversarial_Attacks_in_the_Making_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Mind_the_Gap_CVPR_2025_supplemental.pdf
|
2503.02986
|
@InProceedings{Park_2025_CVPR,
author = {Park, Jeonghwan and McLaughlin, Niall and Alouani, Ihsen},
title = {Mind the Gap: Detecting Black-box Adversarial Attacks in the Making through Query Update Analysis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10235-10243}
}
|
Adversarial attacks remain a significant threat that can jeopardize the integrity of Machine Learning (ML) models. In particular, query-based black-box attacks can generate malicious noise without having access to the victim model's architecture, making them practical in real-world contexts. The community has proposed several defenses against adversarial attacks, only to be broken by more advanced and adaptive attack strategies. In this paper, we propose a framework that detects if an adversarial noise instance is being generated. Unlike existing stateful defenses that detect adversarial noise generation by monitoring the input space, our approach learns adversarial patterns in the input update similarity space. In fact, we propose to observe a new metric called Delta Similarity (DS), which we show it captures more efficiently the adversarial behavior.We evaluate our approach against 8 state-of-the-art attacks, including adaptive attacks, where the adversary is aware of the defense and tries to evade detection. We find that our approach is significantly more robust than existing defenses both in terms of specificity and sensitivity.
|
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] |
578 |
Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph
|
[
"Rao Fu",
"Jianmin Zheng",
"Liang Yu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Consistent_Normal_Orientation_for_3D_Point_Clouds_via_Least_Squares_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Consistent_Normal_Orientation_for_3D_Point_Clouds_via_Least_Squares_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Consistent_Normal_Orientation_CVPR_2025_supplemental.zip
| null |
@InProceedings{Fu_2025_CVPR,
author = {Fu, Rao and Zheng, Jianmin and Yu, Liang},
title = {Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16932-16942}
}
|
The orientation of surface normals in 3D point cloud is a fundamental problem in computer vision and graphics. Determining a globally consistent orientation solely from the point cloud is however challenging due to the global scope of the problem and the discrete nature of point cloud, particularly in the presence of noise, outliers, holes, thin structures, and complex topologies. This paper presents an efficient, robust, and global algorithm for generating consistent normal orientation of a dense 3D point cloud. The basic idea is to transform the original binary normal orientation problem to finding a relaxed sign field on a Delaunay graph, which can be achieved by solving a sparse linear system. The Delaunay graph is constructed by triangulating a level set of an implicit function defined from the input point cloud. The shape diameter function is estimated to serve as a prior for determining an appropriate level value such that the level set implicitly defines the inner and outer shells enclosing the input point clouds. As such, our algorithm leverages the strengths of the shape diameter function, Delaunay triangulation, and the least-square techniques, making the underlying processes take both geometry and topology into consideration, and thus provides an efficient and robust solution for handling point clouds with complicated geometry and topology. Extensive experiments on various shapes with noise and outliers confirm the effectiveness and robustness of our algorithm.
|
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] |
579 |
GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction
|
[
"Sicheng Zuo",
"Wenzhao Zheng",
"Yuanhui Huang",
"Jie Zhou",
"Jiwen Lu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zuo_GaussianWorld_Gaussian_World_Model_for_Streaming_3D_Occupancy_Prediction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zuo_GaussianWorld_Gaussian_World_Model_for_Streaming_3D_Occupancy_Prediction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zuo_GaussianWorld_Gaussian_World_CVPR_2025_supplemental.zip
|
2412.10373
|
@InProceedings{Zuo_2025_CVPR,
author = {Zuo, Sicheng and Zheng, Wenzhao and Huang, Yuanhui and Zhou, Jie and Lu, Jiwen},
title = {GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6772-6781}
}
|
3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and ignore the strong prior provided by the evolution of 3D scenes (e.g., only dynamic objects move). In this paper, we propose a world-modelbased framework to exploit the scene evolution for perception. We reformulate 3D occupancy prediction as a 4D occupancy forecasting problem conditioned on the current sensor input. We decompose the scene evolution into three factors: 1) ego motion alignment of static scenes; 2) local movements of dynamic objects; and 3) completion of newly-observed scenes. We then employ a Gaussian world model (GaussianWorld) to explicitly exploit these priors and infer the scene evolution in the 3D Gaussian space considering the current RGB observation. We evaluate the effectiveness of our framework on the widely used nuScenes dataset. Our GaussianWorld improves the performance of the single-frame counterpart by over 2% in mIoU without introducing additional computations.
|
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] |
580 |
ICP: Immediate Compensation Pruning for Mid-to-high Sparsity
|
[
"Xin Luo",
"Xueming Fu",
"Zihang Jiang",
"S. Kevin Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_ICP_Immediate_Compensation_Pruning_for_Mid-to-high_Sparsity_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_ICP_Immediate_Compensation_Pruning_for_Mid-to-high_Sparsity_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_ICP_Immediate_Compensation_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Luo_2025_CVPR,
author = {Luo, Xin and Fu, Xueming and Jiang, Zihang and Zhou, S. Kevin},
title = {ICP: Immediate Compensation Pruning for Mid-to-high Sparsity},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9487-9496}
}
|
The increasing adoption of large-scale models under 7 billion parameters in both language and vision domains enables inference tasks on a single consumer-grade GPU but makes fine-tuning models of this scale, especially 7B models, challenging. This limits the applicability of pruning methods that require full fine-tuning. Meanwhile, pruning methods that do not require fine-tuning perform well at low sparsity levels (10%-50%) but struggle at mid-to-high sparsity levels (50%-70%), where the error behaves equivalently to that of semi-structured pruning. To address these issues, this paper introduces ICP, which finds a balance between full fine-tuning and zero fine-tuning. First, Sparsity Rearrange is used to reorganize the predefined sparsity levels, followed by Block-wise Compensate Pruning, which alternates pruning and compensation on the model's backbone, fully utilizing inference results while avoiding full model fine-tuning. Experiments show that ICP improves performance at mid-to-high sparsity levels compared to baselines, with only a slight increase in pruning time and no additional peak memory overhead.
|
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-0.08505630493164062,
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] |
581 |
VinaBench: Benchmark for Faithful and Consistent Visual Narratives
|
[
"Silin Gao",
"Sheryl Mathew",
"Li Mi",
"Sepideh Mamooler",
"Mengjie Zhao",
"Hiromi Wakaki",
"Yuki Mitsufuji",
"Syrielle Montariol",
"Antoine Bosselut"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_VinaBench_Benchmark_for_Faithful_and_Consistent_Visual_Narratives_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_VinaBench_Benchmark_for_Faithful_and_Consistent_Visual_Narratives_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_VinaBench_Benchmark_for_CVPR_2025_supplemental.pdf
|
2503.20871
|
@InProceedings{Gao_2025_CVPR,
author = {Gao, Silin and Mathew, Sheryl and Mi, Li and Mamooler, Sepideh and Zhao, Mengjie and Wakaki, Hiromi and Mitsufuji, Yuki and Montariol, Syrielle and Bosselut, Antoine},
title = {VinaBench: Benchmark for Faithful and Consistent Visual Narratives},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2870-2879}
}
|
Visual narrative generation transforms textual narratives into sequences of images illustrating the content of the text. However, generating visual narratives that are faithful to the input text and self-consistent across generated images remains an open challenge, due to the lack of knowledge constraints used for planning the stories. In this work, we propose a new benchmark, VinaBench, to address this challenge. Our benchmark annotates the underlying commonsense and discourse constraints in visual narrative samples, offering systematic scaffolds for learning the implicit strategies of visual storytelling. Based on the incorporated narrative constraints, we further propose novel metrics to closely evaluate the consistency of generated narrative images and the alignment of generations with the input textual narrative. Our results across three generative vision models demonstrate that learning with VinaBench's knowledge constraints effectively improves the faithfulness and cohesion of generated visual narratives.
|
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] |
582 |
ATA: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting
|
[
"Yizhe Tang",
"Zhimin Sun",
"Yuzhen Du",
"Ran Yi",
"Guangben Lu",
"Teng Hu",
"Luying Li",
"Lizhuang Ma",
"Fangyuan Zou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_ATA_Adaptive_Transformation_Agent_for_Text-Guided_Subject-Position_Variable_Background_Inpainting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_ATA_Adaptive_Transformation_Agent_for_Text-Guided_Subject-Position_Variable_Background_Inpainting_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_ATA_Adaptive_Transformation_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Tang_2025_CVPR,
author = {Tang, Yizhe and Sun, Zhimin and Du, Yuzhen and Yi, Ran and Lu, Guangben and Hu, Teng and Li, Luying and Ma, Lizhuang and Zou, Fangyuan},
title = {ATA: Adaptive Transformation Agent for Text-Guided Subject-Position Variable Background Inpainting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18335-18345}
}
|
Image inpainting aims to fill the missing region of an image.Recently, there has been a surge of interest in foreground-conditioned background inpainting, a sub-task that fills the background of an image while the foreground subject and associated text prompt are provided.Existing background inpainting methods typically strictly preserve the subject's original position from the source image,resulting in inconsistencies between the subject and the generated background.To address this challenge, we propose a new task, the "Text-Guided Subject-Position Variable Background Inpainting", which aims to dynamically adjust the subject position to achieve a harmonious relationship between the subject andthe inpainted background, and propose the Adaptive Transformation Agent (A^\text T A) for this task.Firstly, we design a PosAgent Block that adaptively predicts an appropriate displacement based on given features to achieve variable subject-position.Secondly, we design the Reverse Displacement Transform (RDT) module, which arranges multiple PosAgent blocks in a reverse structure, to transform hierarchical feature maps from deep to shallow based on semantic information.Thirdly, we equip A^\text T A with a Position Switch Embedding to control whether the subject's position in the generated image is adaptively predicted or fixed.Extensive comparative experiments validate the effectiveness of our A^\text T A approach, which not only demonstrates superior inpainting capabilities in subject-position variable inpainting, but also ensures good performance on subject-position fixed inpainting.
|
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] |
583 |
Optimizing for the Shortest Path in Denoising Diffusion Model
|
[
"Ping Chen",
"Xingpeng Zhang",
"Zhaoxiang Liu",
"Huan Hu",
"Xiang Liu",
"Kai Wang",
"Min Wang",
"Yanlin Qian",
"Shiguo Lian"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Optimizing_for_the_Shortest_Path_in_Denoising_Diffusion_Model_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Optimizing_for_the_Shortest_Path_in_Denoising_Diffusion_Model_CVPR_2025_paper.pdf
| null |
2503.03265
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Ping and Zhang, Xingpeng and Liu, Zhaoxiang and Hu, Huan and Liu, Xiang and Wang, Kai and Wang, Min and Qian, Yanlin and Lian, Shiguo},
title = {Optimizing for the Shortest Path in Denoising Diffusion Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18021-18030}
}
|
In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior methods. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://github.com/UnicomAI/ShortDF.
|
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] |
584 |
Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception
|
[
"Yuanchen Wu",
"Lu Zhang",
"Hang Yao",
"Junlong Du",
"Ke Yan",
"Shouhong Ding",
"Yunsheng Wu",
"Xiaoqiang Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Antidote_A_Unified_Framework_for_Mitigating_LVLM_Hallucinations_in_Counterfactual_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Antidote_A_Unified_Framework_for_Mitigating_LVLM_Hallucinations_in_Counterfactual_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Antidote_A_Unified_CVPR_2025_supplemental.pdf
|
2504.20468
|
@InProceedings{Wu_2025_CVPR,
author = {Wu, Yuanchen and Zhang, Lu and Yao, Hang and Du, Junlong and Yan, Ke and Ding, Shouhong and Wu, Yunsheng and Li, Xiaoqiang},
title = {Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14646-14656}
}
|
Large Vision-Language Models (LVLMs) have achieved impressive results across various cross-modal tasks. However, hallucinations, i.e., the models generating counterfactual responses, remain a challenge. Though recent studies have attempted to alleviate object perception hallucinations, they focus on the models' response generation, and overlook the task question itself. This paper discusses the vulnerability of LVLMs in solving counterfactual presupposition questions (CPQs), where the models are prone to accept the presuppositions of counterfactual objects and produce severe hallucinatory responses. To this end, we introduce "Antidote", a unified, synthetic data-driven post-training framework for mitigating both types of hallucination above. It leverages synthetic data to incorporate factual priors into questions to achieve self-correction, and decouples the mitigation process into a preference optimization problem. Furthermore, we construct "CP-Bench", a novel benchmark to evaluate LVLMs' ability to correctly handle CPQs and produce factual responses. Applied to the LLaVA series, Antidote can simultaneously enhance performance on CP-Bench by over 50%, POPE by 1.8-3.3%, and CHAIR & SHR by 30-50%, all without relying on external supervision from stronger LVLMs or human feedback and without introducing noticeable catastrophic forgetting issues.
|
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] |
585 |
Language-Guided Audio-Visual Learning for Long-Term Sports Assessment
|
[
"Huangbiao Xu",
"Xiao Ke",
"Huanqi Wu",
"Rui Xu",
"Yuezhou Li",
"Wenzhong Guo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Language-Guided_Audio-Visual_Learning_for_Long-Term_Sports_Assessment_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Language-Guided_Audio-Visual_Learning_for_Long-Term_Sports_Assessment_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Language-Guided_Audio-Visual_Learning_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Xu_2025_CVPR,
author = {Xu, Huangbiao and Ke, Xiao and Wu, Huanqi and Xu, Rui and Li, Yuezhou and Guo, Wenzhong},
title = {Language-Guided Audio-Visual Learning for Long-Term Sports Assessment},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23967-23977}
}
|
Long-term sports assessment is a challenging task in video understanding since it requires judging complex movement variations and action-music coordination. However, there is no direct correlation between the diverse background music and movements in sporting events. Previous works require a large number of model parameters to learn potential associations between actions and music. To address this issue, we propose a language-guided audio-visual learning (MLAVL) framework that models "audio-action-visual" correlations guided by low-cost language modality. In our framework, multidimensional domain-based actions form action knowledge graphs, motivating audio-visual modalities to focus on task-relevant actions. We further design a shared-specific context encoder to integrate deep multimodal semantics, and an audio-visual cross-modal fusion module to evaluate action-music consistency. To match the sport's rules, we then propose a dual-branch prompt-guided grading module to weigh both visual and audio-visual performance. Extensive experiments demonstrate that our approach achieves state-of-the-art on four public long-term sports benchmarks while maintaining low parameters. Our code is available at https://github.com/XuHuangbiao/MLAVL.
|
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] |
586 |
Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration
|
[
"Jae Hyeon Park",
"Joo Hyeon Jeon",
"Jae Yun Lee",
"Sangyeon Ahn",
"Min Hee Cha",
"Min Geol Kim",
"Hyeok Nam",
"Sung In Cho"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Dynamic_Pseudo_Labeling_via_Gradient_Cutting_for_High-Low_Entropy_Exploration_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Dynamic_Pseudo_Labeling_via_Gradient_Cutting_for_High-Low_Entropy_Exploration_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Dynamic_Pseudo_Labeling_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Park_2025_CVPR,
author = {Park, Jae Hyeon and Jeon, Joo Hyeon and Lee, Jae Yun and Ahn, Sangyeon and Cha, Min Hee and Kim, Min Geol and Nam, Hyeok and Cho, Sung In},
title = {Dynamic Pseudo Labeling via Gradient Cutting for High-Low Entropy Exploration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20602-20611}
}
|
This study addresses the limitations of existing dynamic pseudo-labeling (DPL) techniques, which often utilize static or dynamic thresholds for confident sample selection. The existing methods fail to capture the non-linear relationship between task accuracy and model confidence, particularly in the context of overconfidence. This can limit the model's learning opportunities for high entropy samples that significantly influence a model's generalization ability. To solve this, we propose a novel gradient pass-based DPL technique that incorporates the high-entropy samples, which are typically overlooked. Our approach introduces two classifiers--low gradient pass (LGP) and high gradient pass (HGP)--to derive over- and under-confident dynamic thresholds that indicate the class-wise overconfidence acceleration, respectively. By combining the under- and over-confident states from the GP classifiers, we create a more adaptive and accurate PL method. Our main contributions highlight the importance of considering both low and high-confidence samples in enhancing the model's robustness and generalization for improved PL performance.
|
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] |
587 |
VODiff: Controlling Object Visibility Order in Text-to-Image Generation
|
[
"Dong Liang",
"Jinyuan Jia",
"Yuhao Liu",
"Zhanghan Ke",
"Hongbo Fu",
"Rynson W. H. Lau"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_VODiff_Controlling_Object_Visibility_Order_in_Text-to-Image_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_VODiff_Controlling_Object_Visibility_Order_in_Text-to-Image_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_VODiff_Controlling_Object_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liang_2025_CVPR,
author = {Liang, Dong and Jia, Jinyuan and Liu, Yuhao and Ke, Zhanghan and Fu, Hongbo and Lau, Rynson W. H.},
title = {VODiff: Controlling Object Visibility Order in Text-to-Image Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18379-18389}
}
|
Recent advancements in diffusion models have significantly enhanced the performance of text-to-image models in image synthesis. To enable control over the the spatial locations of the generated objects,diffusion-based methods typically utilizeobject layout as an auxiliary input. However, we observe that this approach treats all objects as being on the same layer and neglect their visibility order, leading to the synthesis of overlapping objects with incorrect occlusions.To address this limitation, we introduce in this paper a new training-free framework that considers object visibility order explicitly and allows users to place overlapping objects in a stack of layers. Our framework consists of two visibility-based designs. First, we propose a novel Sequential Denoising Process (SDP) to divide the whole image generation into multiple stages for different objects, each stage primarily focuses on an object. Second, we propose a novel Visibility-Order-Aware (VOA) Loss to transform the layout and occlusion constraints into an attention map optimization process to improve the accuracy of synthesizing object occlusions in complex scenes. By merging these two novel components, our framework, dubbed VODiff, enables the generation of photorealistic images that satisfy user-specified spatial constraints and object occlusion relationships. In addition, we introduce VOBench, a diverse benchmark dataset containing 200 curated samples, each with a reference image, text prompts, object visibility orders and layout maps. We conduct extensive evaluations on this dataset to demonstrate the superiority of our approach.
|
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] |
588 |
Dual Diffusion for Unified Image Generation and Understanding
|
[
"Zijie Li",
"Henry Li",
"Yichun Shi",
"Amir Barati Farimani",
"Yuval Kluger",
"Linjie Yang",
"Peng Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Dual_Diffusion_for_Unified_Image_Generation_and_Understanding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Dual_Diffusion_for_Unified_Image_Generation_and_Understanding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Dual_Diffusion_for_CVPR_2025_supplemental.pdf
|
2501.00289
|
@InProceedings{Li_2025_CVPR,
author = {Li, Zijie and Li, Henry and Shi, Yichun and Farimani, Amir Barati and Kluger, Yuval and Yang, Linjie and Wang, Peng},
title = {Dual Diffusion for Unified Image Generation and Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2779-2790}
}
|
Diffusion models have gained tremendous success in text-to-image generation, yet still struggle with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities. Inspired by the multimodal diffusion transformer (MM-DiT) and recent advances in discrete diffusion language modeling, we leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly under a single loss function, which is back-propagated through both branches of the diffusion transformer. The resulting model is highly flexible and capable of a wide range of tasks including image generation, captioning, and visual question answering. Our model attained competitive performance compared to recent unified image understanding and generation models, demonstrating the potential of multimodal diffusion modeling as a promising alternative to autoregressive next-token prediction models.
|
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] |
589 |
WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation
|
[
"Silin Cheng",
"Yang Liu",
"Xinwei He",
"Sebastien Ourselin",
"Lei Tan",
"Gen Luo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_WeakMCN_Multi-task_Collaborative_Network_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_WeakMCN_Multi-task_Collaborative_Network_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_WeakMCN_Multi-task_Collaborative_CVPR_2025_supplemental.pdf
|
2505.18686
|
@InProceedings{Cheng_2025_CVPR,
author = {Cheng, Silin and Liu, Yang and He, Xinwei and Ourselin, Sebastien and Tan, Lei and Luo, Gen},
title = {WeakMCN: Multi-task Collaborative Network for Weakly Supervised Referring Expression Comprehension and Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9175-9185}
}
|
Weakly supervised referring expression comprehension (WREC) and segmentation (WRES) aim to learn object grounding based on a given expression using weak supervision signals like image-text pairs. While these tasks have traditionally been modeled separately, we argue that they can benefit from joint learning in a multi-task framework. To this end, we propose WeakMCN, a novel multi-task collaborative network that effectively combines WREC and WRES with a dual-branch architecture. Specifically, the WREC branch is formulated as anchor-based contrastive learning, which also acts as a teacher to supervise the WRES branch. In WeakMCN, we propose two innovative designs to facilitate multi-task collaboration, namely Dynamic Visual Feature Enhancement (DVFE) and Collaborative Consistency Module (CCM). DVFE dynamically combines various pre-trained visual knowledge to meet different task requirements, while CCM promotes cross-task consistency from the perspective of optimization. Extensive experimental results on three popular REC and RES benchmarks, i.e., RefCOCO, RefCOCO+, and RefCOCOg, consistently demonstrate performance gains of WeakMCN over state-of-the-art single-task alternatives, e.g., up to 3.91% and 13.11% on RefCOCO for WREC and WRES tasks, respectively. Furthermore, experiments also validate the strong generalization ability of WeakMCN in both semi-supervised REC and RES settings against existing methods, e.g., +8.94% for semi-REC and +7.71% for semi-RES on 1% RefCOCO.
|
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] |
590 |
CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation
|
[
"Jiahao Li",
"Weijian Ma",
"Xueyang Li",
"Yunzhong Lou",
"Guichun Zhou",
"Xiangdong Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CAD-Llama_Leveraging_Large_Language_Models_for_Computer-Aided_Design_Parametric_3D_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CAD-Llama_Leveraging_Large_Language_Models_for_Computer-Aided_Design_Parametric_3D_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CAD-Llama_Leveraging_Large_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Li_2025_CVPR,
author = {Li, Jiahao and Ma, Weijian and Li, Xueyang and Lou, Yunzhong and Zhou, Guichun and Zhou, Xiangdong},
title = {CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18563-18573}
}
|
Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-specific areas. This study investigates the generation of parametric sequences for computer-aided design (CAD) models using LLMs. This endeavor represents an initial step towards creating parametric 3D shapes with LLMs, as CAD model parameters directly correlate with shapes in three-dimensional space. Despite the formidable generative capacities of LLMs, this task remains challenging, as these models neither encounter parametric sequences during their pretraining phase nor possess direct awareness of 3D structures. To address this, we present CAD-Llama, a framework designed to enhance pretrained LLMs for generating parametric 3D CAD models. Specifically, we develop a hierarchical annotation pipeline and a code-like format to translate parametric 3D CAD command sequences into Structured Parametric CAD Code (SPCC), incorporating hierarchical semantic descriptions. Furthermore, we propose an adaptive pretraining approach utilizing SPCC, followed by an instruction tuning process aligned with CAD-specific guidelines. This methodology aims to equip LLMs with the spatial knowledge inherent in parametric sequences. Experimental results demonstrate that our framework significantly outperforms prior autoregressive methods and existing LLM baselines.
|
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] |
591 |
Leveraging SD Map to Augment HD Map-based Trajectory Prediction
|
[
"Zhiwei Dong",
"Ran Ding",
"Wei Li",
"Peng Zhang",
"Guobin Tang",
"Jia Guo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Leveraging_SD_Map_to_Augment_HD_Map-based_Trajectory_Prediction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Leveraging_SD_Map_to_Augment_HD_Map-based_Trajectory_Prediction_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Dong_2025_CVPR,
author = {Dong, Zhiwei and Ding, Ran and Li, Wei and Zhang, Peng and Tang, Guobin and Guo, Jia},
title = {Leveraging SD Map to Augment HD Map-based Trajectory Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17219-17228}
}
|
Latest trajectory prediction models in real-world autonomous driving systems often rely on online High-Definition (HD) maps to understand the road environment.However, online HD maps suffer from perception errors and feature redundancy, which hinder the performance of HD map-based trajectory prediction models.To address these issues, we introduce a framework, termed SD map-Augmented Trajectory Prediction (SATP), which leverages Standard-Definition (SD) maps to enhance HD map-based trajectory prediction models.First, we propose an SD-HD fusion approach to leverage SD maps across the diverse range of HD map-based trajectory prediction models. Second, we design a novel AlignNet to align the SD map with the HD map, further improving the effectiveness of SD maps. Experiments on real-world autonomous driving benchmarks demonstrate that SATP not only improves the performance of HD map-based trajectory prediction up to 25% in real-world scenarios using online HD maps but also brings benefits in ideal scenarios with ground-truth HD maps.
|
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] |
592 |
4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video
|
[
"Qiang Hu",
"Zihan Zheng",
"Houqiang Zhong",
"Sihua Fu",
"Li Song",
"Xiaoyun Zhang",
"Guangtao Zhai",
"Yanfeng Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_4DGC_Rate-Aware_4D_Gaussian_Compression_for_Efficient_Streamable_Free-Viewpoint_Video_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_4DGC_Rate-Aware_4D_Gaussian_Compression_for_Efficient_Streamable_Free-Viewpoint_Video_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_4DGC_Rate-Aware_4D_CVPR_2025_supplemental.zip
|
2503.18421
|
@InProceedings{Hu_2025_CVPR,
author = {Hu, Qiang and Zheng, Zihan and Zhong, Houqiang and Fu, Sihua and Song, Li and Zhang, Xiaoyun and Zhai, Guangtao and Wang, Yanfeng},
title = {4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {875-885}
}
|
3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences. However, the vast number of Gaussians and their associated attributes poses significant challenges for storage and transmission. Existing methods typically handle dynamic 3DGS representation and compression separately, neglecting motion information and the rate-distortion (RD) trade-off during training, leading to performance degradation and increased model redundancy. To address this gap, we propose 4DGC, a novel rate-aware 4D Gaussian compression framework that significantly reduces storage size while maintaining superior RD performance for FVV. Specifically, 4DGC introduces a motion-aware dynamic Gaussian representation that utilizes a compact motion grid combined with sparse compensated Gaussians to exploit inter-frame similarities. This representation effectively handles large motions, preserving quality and reducing temporal redundancy. Furthermore, we present an end-to-end compression scheme that employs differentiable quantization and a tiny implicit entropy model to compress the motion grid and compensated Gaussians efficiently. The entire framework is jointly optimized using a rate-distortion trade-off. Extensive experiments demonstrate that 4DGC supports variable bitrates and consistently outperforms existing methods in RD performance across multiple datasets.
|
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] |
593 |
ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation
|
[
"Tao Tan",
"Qiulei Dong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_ONDA-Pose_Occlusion-Aware_Neural_Domain_Adaptation_for_Self-Supervised_6D_Object_Pose_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_ONDA-Pose_Occlusion-Aware_Neural_Domain_Adaptation_for_Self-Supervised_6D_Object_Pose_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Tan_2025_CVPR,
author = {Tan, Tao and Dong, Qiulei},
title = {ONDA-Pose: Occlusion-Aware Neural Domain Adaptation for Self-Supervised 6D Object Pose Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16829-16838}
}
|
Self-supervised 6D object pose estimation has received increasing attention in computer vision recently. Some typical works in literature attempt to translate the synthetic images with object pose labels generated by object CAD models into the real domain, and then use the translated data for training. However, their performance is generally limited, since (i) there still exists a domain gap between the translated images and the real images and (ii) the translated images can not sufficiently reflect occlusions that exist in many real images. To address these problems, we propose an Occlusion-Aware Neural Domain Adaptation method for self-supervised 6D object Pose estimation, called ONDA-Pose. The proposed method comprises three main steps. Firstly, by utilizing both the training real images without pose labels and a CAD model, we explore a CAD-like radiance field for rendering corresponding synthetic images that have similar textures to those generated by the CAD model. Then, a backbone pose estimator trained on the synthetic data is employed to provide initial pose estimations for the synthetic images rendered from the CAD-like radiance field, and the initial object poses are refined by a global object pose refiner to generate pseudo object pose labels. Finally, the backbone pose estimator is further self-supervised as the final pose estimator by jointly utilizing the real images with pseudo object pose labels and the synthetic images rendered from the CAD-like radiance field. Experimental results on three public datasets demonstrate that ONDA-Pose significantly outperforms the comparative state-of-the-art methods in most cases.
|
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] |
594 |
Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation
|
[
"Dingcheng Zhen",
"Shunshun Yin",
"Shiyang Qin",
"Hou Yi",
"Ziwei Zhang",
"Siyuan Liu",
"Gan Qi",
"Ming Tao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhen_Teller_Real-Time_Streaming_Audio-Driven_Portrait_Animation_with_Autoregressive_Motion_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhen_Teller_Real-Time_Streaming_Audio-Driven_Portrait_Animation_with_Autoregressive_Motion_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhen_Teller_Real-Time_Streaming_CVPR_2025_supplemental.zip
|
2503.18429
|
@InProceedings{Zhen_2025_CVPR,
author = {Zhen, Dingcheng and Yin, Shunshun and Qin, Shiyang and Yi, Hou and Zhang, Ziwei and Liu, Siyuan and Qi, Gan and Tao, Ming},
title = {Teller: Real-Time Streaming Audio-Driven Portrait Animation with Autoregressive Motion Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21075-21085}
}
|
In this work, we introduce the first autoregressive framework for real-time, audio-driven portrait animation, a.k.a, talking head. Beyond the challenge of lengthy animation times, a critical challenge in realistic talking head generation lies in preserving the natural movement of diverse body parts. To this end, we propose Teller, the first streaming audio-driven protrait animation framework with autoregressive motion generation. Specifically, Teller first decomposes facial and body detail animation into two components: Facial Motion Latent Generation (FMLG) based on an autoregressive transfromer, and movement authenticity refinement using a Efficient Temporal Module (ETM).Concretely, FMLG employs a Residual VQ model to map the facial motion latent from the implicit keypoint-based model into discrete motion tokens, which are then temporally sliced with audio embeddings. This enables the AR tranformer to learn real-time, stream-based mappings from audio to motion.Furthermore, Teller incorporate ETM to capture finer motion details. This module ensures the physical consistency of body parts and accessories, such as neck muscles and earrings, improving the realism of these movements.Teller is designed to be efficient, surpassing the inference speed of diffusion-based models (Hallo 20.93s vs. Teller 0.92s for one second video generation), and achieves a real-time streaming performance of up to 25 FPS. Extensive experiments demonstrate that our method outperforms recent audio-driven portrait animation models, especially in small movements, as validated by human evaluations with a significant margin in quality and realism.
|
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] |
595 |
GASP: Gaussian Avatars with Synthetic Priors
|
[
"Jack Saunders",
"Charlie Hewitt",
"Yanan Jian",
"Marek Kowalski",
"Tadas Baltrusaitis",
"Yiye Chen",
"Darren Cosker",
"Virginia Estellers",
"Nicholas Gydé",
"Vinay P. Namboodiri",
"Benjamin E. Lundell"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Saunders_GASP_Gaussian_Avatars_with_Synthetic_Priors_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Saunders_GASP_Gaussian_Avatars_with_Synthetic_Priors_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Saunders_GASP_Gaussian_Avatars_CVPR_2025_supplemental.zip
|
2412.07739
|
@InProceedings{Saunders_2025_CVPR,
author = {Saunders, Jack and Hewitt, Charlie and Jian, Yanan and Kowalski, Marek and Baltrusaitis, Tadas and Chen, Yiye and Cosker, Darren and Estellers, Virginia and Gyd\'e, Nicholas and Namboodiri, Vinay P. and Lundell, Benjamin E.},
title = {GASP: Gaussian Avatars with Synthetic Priors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {271-280}
}
|
Gaussian Splatting has changed the game for real-time photo-realistic rendering. One of the most popular applications of Gaussian Splatting is to create animatable avatars, known as Gaussian Avatars. Recent works have pushed the boundaries of quality and rendering efficiency but suffer from two main limitations. Either they require expensive multi-camera rigs to produce avatars with free-view rendering, or they can be trained with a single camera but only rendered at high quality from this fixed viewpoint. An ideal model would be trained using a short monocular video or image from available hardware, such as a webcam, and rendered from any view. To this end, we propose GASP: Gaussian Avatars with Synthetic Priors. To overcome the limitations of existing datasets, we exploit the pixel-perfect nature of synthetic data to train a Gaussian Avatar prior. By fitting this prior model to a single photo or videoand fine-tuning it, we get a high-quality Gaussian Avatar, which supports 360^\circ rendering. Our prior is only required for fitting, not inference, enabling real-time application. Through our method, we obtain high-quality, animatable Avatars from limited data which can be animated and rendered at 70fps on commercial hardware.
|
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] |
596 |
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
|
[
"Jie Liu",
"Tiexin Qin",
"Hui Liu",
"Yilei Shi",
"Lichao Mou",
"Xiao Xiang Zhu",
"Shiqi Wang",
"Haoliang Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Q-PART_Quasi-Periodic_Adaptive_Regression_with_Test-time_Training_for_Pediatric_Left_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Q-PART_Quasi-Periodic_Adaptive_Regression_with_Test-time_Training_for_Pediatric_Left_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Q-PART_Quasi-Periodic_Adaptive_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Jie and Qin, Tiexin and Liu, Hui and Shi, Yilei and Mou, Lichao and Zhu, Xiao Xiang and Wang, Shiqi and Li, Haoliang},
title = {Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15560-15569}
}
|
In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this task, they suffer from two significant limitations. Existing TTT works are primarily designed for classification tasks rather than continuous value regression, and they lack mechanisms to handle the quasi-periodic nature of cardiac signals. To tackle these issues, we propose a novel Quasi-Periodic Adaptive Regression with Test-time Training (Q-PART) framework. In the training stage, the proposed Quasi-Period Network decomposes the echocardiogram into periodic and aperiodic components within latent space by combining parameterized helix trajectories with Neural Controlled Differential Equations. During inference, our framework further employs a variance minimization strategy across image augmentations that simulate common quality issues in echocardiogram acquisition, along with differential adaptation rates for periodic and aperiodic components. Theoretical analysis is provided to demonstrate that our variance minimization objective effectively bounds the regression error under mild conditions. Furthermore, extensive experiments across three pediatric age groups demonstrate that Q-PART not only significantly outperforms existing approaches in pediatric LVEF prediction, but also exhibits strong clinical screening capability with high mAUROC scores (up to 0.9747) and maintains gender-fair performance across all metrics, validating its robustness and practical utility in pediatric echocardiography analysis.
|
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] |
597 |
Composing Parts for Expressive Object Generation
|
[
"Harsh Rangwani",
"Aishwarya Agarwal",
"Kuldeep Kulkarni",
"R. Venkatesh Babu",
"Srikrishna Karanam"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Rangwani_Composing_Parts_for_Expressive_Object_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Rangwani_Composing_Parts_for_Expressive_Object_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rangwani_Composing_Parts_for_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Rangwani_2025_CVPR,
author = {Rangwani, Harsh and Agarwal, Aishwarya and Kulkarni, Kuldeep and Babu, R. Venkatesh and Karanam, Srikrishna},
title = {Composing Parts for Expressive Object Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13209-13219}
}
|
Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, like Stable Diffusion, cannot handle fine-grained part-level attributes in the text prompts. Specifically, when additional attribute details are added to the base text prompt, these text-to-image models either generate an image vastly different from the image generated from the base prompt or ignore the attribute details. To mitigate these issues, we introduce PartComposer, a training-free method that enables image generation based on fine-grained part-level attributes specified for objects in the base text prompt. This allows more control for artists and enables novel object compositions by combining distinctive object parts. PartComposer first localizes object parts by denoising the object region from a specific diffusion process. This enables each part token to be localized to the right region. After obtaining part masks, we run a localized diffusion process in each part region based on fine-grained part attributes and combine them to produce the final image. All stages of PartComposer are based on repurposing a pre-trained diffusion model, which enables it to generalize across domains. We demonstrate the effectiveness of part-level control provided by PartComposer through qualitative visual examples and quantitative comparisons with contemporary baselines.
|
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] |
598 |
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-Scale Reinforcement Learning in Autonomous Driving
|
[
"Dongkun Zhang",
"Jiaming Liang",
"Ke Guo",
"Sha Lu",
"Qi Wang",
"Rong Xiong",
"Zhenwei Miao",
"Yue Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_CarPlanner_Consistent_Auto-regressive_Trajectory_Planning_for_Large-Scale_Reinforcement_Learning_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_CarPlanner_Consistent_Auto-regressive_Trajectory_Planning_for_Large-Scale_Reinforcement_Learning_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_CarPlanner_Consistent_Auto-regressive_CVPR_2025_supplemental.pdf
|
2502.19908
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Dongkun and Liang, Jiaming and Guo, Ke and Lu, Sha and Wang, Qi and Xiong, Rong and Miao, Zhenwei and Wang, Yue},
title = {CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-Scale Reinforcement Learning in Autonomous Driving},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17239-17248}
}
|
Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement learning (RL), have shown promise in specific scenarios, RL planners struggle with training inefficiencies and managing large-scale, real-world driving scenarios.In this paper, we introduce CarPlanner, a Consistent auto-regressive Planner that uses RL to generate multi-modal trajectories. The auto-regressive structure enables efficient large-scale RL training, while the incorporation of consistency ensures stable policy learning by maintaining coherent temporal consistency across time steps. Moreover, CarPlanner employs a generation-selection framework with an expert-guided reward function and an invariant-view module, simplifying RL training and enhancing policy performance.Extensive analysis demonstrates that our proposed RL framework effectively addresses the challenges of training efficiency and performance enhancement, positioning CarPlanner as a promising solution for trajectory planning in autonomous driving.To the best of our knowledge, we are the first to demonstrate that the RL-based planner can surpass both IL- and rule-based state-of-the-arts (SOTAs) on the challenging large-scale real-world dataset nuPlan. Our proposed CarPlanner surpasses RL-, IL-, and rule-based SOTA approaches within this demanding dataset.
|
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] |
599 |
Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation
|
[
"Yiming Qin",
"Zhu Xu",
"Yang Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Apply_Hierarchical-Chain-of-Generation_to_Complex_Attributes_Text-to-3D_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Apply_Hierarchical-Chain-of-Generation_to_Complex_Attributes_Text-to-3D_Generation_CVPR_2025_paper.pdf
| null |
2505.05505
|
@InProceedings{Qin_2025_CVPR,
author = {Qin, Yiming and Xu, Zhu and Liu, Yang},
title = {Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18521-18530}
}
|
Recent text-to-3D generation models have demonstrated remarkable abilities in producing high-quality 3D assets. Despite their great advancements, current models struggle to generate satisfying 3D objects with complex attributes. The difficulty for such complex attributes 3D generation arises from two aspects: (1) existing text-to-3D approaches typically lift text-to-image models to extract semantics via text encoders, while the text encoder exhibits limited comprehension ability for long descriptions, leading to deviated cross-attention focus, subsequently wrong attribute binding in generated results. (2) Objects with complex attributes often exhibit occlusion relationships between different parts, which demands a reasonable generation order as well as explicit disentanglement of different parts to enable structural coherent and attribute following results. Though some works introduce manual efforts to alleviate the above issues, their quality is unstable and highly reliant on manual information. To tackle above problems, we propose a automated method Hierarchical-Chain-of-Generation (HCoG). It leverages a large language model to analyze the long description, decomposes it into several blocks representing different object parts, and organizes an optimal generation order from in to out according to the occlusion relationship between parts, turning the whole generation process into a hierarchical chain. For optimization within each block, we first generate the necessary components coarsely, then bind their attributes precisely by target region localization and corresponding 3D Gaussian kernel optimization. For optimization between blocks, we introduce Gaussian Extension and Label Elimination to seamlessly generate new parts by extending new Gaussian kernels, re-assigning semantic labels, and eliminating unnecessary kernels, ensuring that only relevant parts are added without disrupting previously optimized parts. Experiments validate HCoG's effectiveness in handling complex attributes 3D assets and witnesses high-quality results. The code is available at https://github.com/Wakals/GASCOL.
|
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