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100 |
SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization
|
[
"Hongrui Jia",
"Chaoya Jiang",
"Haiyang Xu",
"Wei Ye",
"Mengfan Dong",
"Ming Yan",
"Ji Zhang",
"Fei Huang",
"Shikun Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_SymDPO_Boosting_In-Context_Learning_of_Large_Multimodal_Models_with_Symbol_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_SymDPO_Boosting_In-Context_Learning_of_Large_Multimodal_Models_with_Symbol_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_SymDPO_Boosting_In-Context_CVPR_2025_supplemental.zip
|
2411.11909
|
@InProceedings{Jia_2025_CVPR,
author = {Jia, Hongrui and Jiang, Chaoya and Xu, Haiyang and Ye, Wei and Dong, Mengfan and Yan, Ming and Zhang, Ji and Huang, Fei and Zhang, Shikun},
title = {SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9361-9371}
}
|
As language models continue to scale, Large Language Models (LLMs) have exhibited emerging capabilities in In-Context Learning (ICL), enabling them to solve language tasks by prefixing a few in-context demonstrations (ICDs) as context. Inspired by these advancements, researchers have extended these techniques to develop Large Multimodal Models (LMMs) with ICL capabilities. However, existing LMMs face a critical issue: they often fail to effectively leverage the visual context in multimodal demonstrations and instead simply follow textual patterns. This indicates that LMMs do not achieve effective alignment between multimodal demonstrations and model outputs. To address this problem, we propose Symbol Demonstration Direct Preference Optimization (SymDPO). Specifically, SymDPO aims to break the traditional paradigm of constructing multimodal demonstrations by using random symbols to replace text answers within instances. This forces the model to carefully understand the demonstration images and establish a relationship between the images and the symbols to answer questions correctly. We validate the effectiveness of this method on multiple benchmarks, demonstrating that with SymDPO, LMMs can more effectively understand the multimodal context within examples and utilize this knowledge to answer questions better. Code is available at https://github.com/APiaoG/SymDPO.
|
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] |
101 |
Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models
|
[
"Zhaoyi Liu",
"Huan Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Stealthy_Backdoor_Attack_in_Self-Supervised_Learning_Vision_Encoders_for_Large_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Stealthy_Backdoor_Attack_in_Self-Supervised_Learning_Vision_Encoders_for_Large_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Stealthy_Backdoor_Attack_CVPR_2025_supplemental.pdf
|
2502.18290
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Zhaoyi and Zhang, Huan},
title = {Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25060-25070}
}
|
Self-supervised learning (SSL) vision encoders learn high-quality image representations and thus have become a vital part of developing vision modality of large vision language models (LVLMs). Due to the high cost of training such encoders, pre-trained encoders are widely shared and deployed into many LVLMs, which are security-critical or bear societal significance. Under this practical scenario, we reveal a new backdoor threat that significant visual hallucinations can be induced into these LVLMs by merely compromising vision encoders. Because of the sharing and reuse of these encoders, many downstream LVLMs may inherit backdoor behaviors from encoders, leading to widespread backdoors. In this work, we propose BadVision, the first method to exploit this vulnerability in SSL vision encoders for LVLMs with novel trigger optimization and backdoor learning techniques. We evaluate BadVision on two types of SSL encoders and LVLMs across eight benchmarks. We show that BadVision effectively drives the LVLMs to attacker-chosen hallucination with over 99% attack success rate, causing a 77.6% relative visual understanding error while maintaining the stealthiness. SoTA backdoor detection methods cannot detect our attack effectively.
|
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] |
102 |
Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior
|
[
"Chanhui Lee",
"Yeonghwan Song",
"Jeany Son"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Data-free_Universal_Adversarial_Perturbation_with_Pseudo-semantic_Prior_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Data-free_Universal_Adversarial_Perturbation_with_Pseudo-semantic_Prior_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Data-free_Universal_Adversarial_CVPR_2025_supplemental.pdf
|
2502.21048
|
@InProceedings{Lee_2025_CVPR,
author = {Lee, Chanhui and Song, Yeonghwan and Son, Jeany},
title = {Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13907-13916}
}
|
Data-free Universal Adversarial Perturbation (UAP) is an image-agnostic adversarial attack that deceives deep neural networks using a single perturbation generated solely from random noise without relying on data priors. However, traditional data-free UAP methods often suffer from limited transferability due to the absence of semantic content in random noise. To address this issue, we propose a novel data-free universal attack method that recursively extracts pseudo-semantic priors directly from the UAPs during training to enrich the semantic content within the data-free UAP framework. Our approach effectively leverages latent semantic information within UAPs via region sampling, enabling successful input transformations--typically ineffective in traditional data-free UAP methods due to the lack of semantic cues--and significantly enhancing black-box transferability. Furthermore, we introduce a sample reweighting technique to mitigate potential imbalances from random sampling and transformations, emphasizing hard examples less affected by the UAPs. Comprehensive experiments on ImageNet show that our method achieves state-of-the-art performance in average fooling rate by a substantial margin, notably improves attack transferability across various CNN architectures compared to existing data-free UAP methods, and even surpasses data-dependent UAP methods. Code is available at: https://github.com/ChnanChan/PSP-UAP.
|
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] |
103 |
Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization
|
[
"Zefeng Zhang",
"Hengzhu Tang",
"Jiawei Sheng",
"Zhenyu Zhang",
"Yiming Ren",
"Zhenyang Li",
"Dawei Yin",
"Duohe Ma",
"Tingwen Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Debiasing_Multimodal_Large_Language_Models_via_Noise-Aware_Preference_Optimization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Debiasing_Multimodal_Large_Language_Models_via_Noise-Aware_Preference_Optimization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Debiasing_Multimodal_Large_CVPR_2025_supplemental.pdf
|
2503.17928
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Zefeng and Tang, Hengzhu and Sheng, Jiawei and Zhang, Zhenyu and Ren, Yiming and Li, Zhenyang and Yin, Dawei and Ma, Duohe and Liu, Tingwen},
title = {Debiasing Multimodal Large Language Models via Noise-Aware Preference Optimization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9423-9433}
}
|
Multimodal Large Language Models (MLLMs) excel in various tasks, yet often struggle with modality bias, tending to rely heavily on a single modality or prior knowledge when generating responses. In this paper, we propose a debiased preference optimization dataset, RLAIF-V-Bias, and introduce a Noise-Aware Preference Optimization (NAPO) algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, prompting the model to overly rely on a specific modality when generating responses. To address the inevitable noise in automatically constructed data, we combine the noise-robust Mean Absolute Error (MAE) with the Binary Cross-Entropy (BCE) in Direct Preference Optimization (DPO) using a negative Box-Cox transformation and dynamically adjust the algorithm's noise robustness based on the evaluated noise levels in the data.Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations.
|
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] |
104 |
SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes
|
[
"Yuji Wang",
"Haoran Xu",
"Yong Liu",
"Jiaze Li",
"Yansong Tang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SAM2-LOVE_Segment_Anything_Model_2_in_Language-aided_Audio-Visual_Scenes_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SAM2-LOVE_Segment_Anything_Model_2_in_Language-aided_Audio-Visual_Scenes_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SAM2-LOVE_Segment_Anything_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Yuji and Xu, Haoran and Liu, Yong and Li, Jiaze and Tang, Yansong},
title = {SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28932-28941}
}
|
Reference Audio-Visual Segmentation (Ref-AVS) aims to provide a pixel-wise scene understanding in Language-aided Audio-Visual Scenes (LAVS). This task requires the model to continuously segment objects referred to by text and audio from a video. Previous dual-modality methods always fail due to the lack of a third modality and the existing triple-modality method struggles with spatio-temporal consistency, leading to the target shift of different frames. In this work, we introduce a novel framework, termed SAM2-LOVE, which integrates textual, audio, and visual representations into a learnable token to prompt and align SAM2 for achieving Ref-AVS in the LAVS. Technically, our approach includes a multimodal fusion module aimed at improving multimodal understanding of SAM2, as well as token propagation and accumulation strategies designed to enhance spatio-temporal consistency without forgetting historical information. We conducted extensive experiments to demonstrate that SAM2-LOVE outperforms the SOTA by 8.5% in J&F on the Ref-AVS benchmark and showcase the simplicity and effectiveness of the components. Our code will be available here.
|
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] |
105 |
GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation
|
[
"Ziqin Huang",
"Gu Wang",
"Chenyangguang Zhang",
"Ruida Zhang",
"Xiu Li",
"Xiangyang Ji"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_GIVEPose_Gradual_Intra-class_Variation_Elimination_for_RGB-based_Category-Level_Object_Pose_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_GIVEPose_Gradual_Intra-class_Variation_Elimination_for_RGB-based_Category-Level_Object_Pose_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_GIVEPose_Gradual_Intra-class_CVPR_2025_supplemental.pdf
|
2503.15110
|
@InProceedings{Huang_2025_CVPR,
author = {Huang, Ziqin and Wang, Gu and Zhang, Chenyangguang and Zhang, Ruida and Li, Xiu and Ji, Xiangyang},
title = {GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22055-22066}
}
|
Recent advances in RGBD-based category-level object pose estimation have been limited by their reliance on precise depth information, restricting their broader applicability. In response, RGB-based methods have been developed. Among these methods, geometry-guided pose regression that originated from instance-level tasks has demonstrated strong performance. However, we argue that the NOCS map is an inadequate intermediate representation for geometry-guided pose regression method, as its many-to-one correspondence with category-level pose introduces redundant instance-specific information, resulting in suboptimal results. This paper identifies the intra-class variation problem inherent in pose regression based solely on the NOCS map and proposes the Intra-class Variation-Free Consensus (IVFC) map, a novel coordinate representation generated from the category-level consensus model. By leveraging the complementary strengths of the NOCS map and the IVFC map, we introduce GIVEPose, a framework that implements Gradual Intra-class Variation Elimination for category-level object pose estimation. Extensive evaluations on both synthetic and real-world datasets demonstrate that GIVEPose significantly outperforms existing state-of-the-art RGB-based approaches, achieving substantial improvements in category-level object pose estimation. Our code is available at https://github.com/ziqin-h/GIVEPose.
|
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] |
106 |
FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video
|
[
"Andrea Boscolo Camiletto",
"Jian Wang",
"Eduardo Alvarado",
"Rishabh Dabral",
"Thabo Beeler",
"Marc Habermann",
"Christian Theobalt"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Camiletto_FRAME_Floor-aligned_Representation_for_Avatar_Motion_from_Egocentric_Video_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Camiletto_FRAME_Floor-aligned_Representation_for_Avatar_Motion_from_Egocentric_Video_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Camiletto_FRAME_Floor-aligned_Representation_CVPR_2025_supplemental.pdf
|
2503.23094
|
@InProceedings{Camiletto_2025_CVPR,
author = {Camiletto, Andrea Boscolo and Wang, Jian and Alvarado, Eduardo and Dabral, Rishabh and Beeler, Thabo and Habermann, Marc and Theobalt, Christian},
title = {FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17497-17507}
}
|
Egocentric motion capture with a head-mounted body-facing stereo camera is crucial for VR and AR applications but presents significant challenges such as heavy occlusions and limited annotated real-world data. Existing methods rely on synthetic pretraining and struggle to generate smooth and accurate predictions in real-world settings, particularly for lower limbs. Our work addresses these limitations by introducing a lightweight VR-based data collection setup with on-board, real-time 6D pose tracking. Using this setup, we collected the most extensive real-world dataset for ego-facing ego-mounted cameras to date in size and motion variability. Effectively integrating this multimodal input -- device pose and camera feeds -- is challenging due to the differing characteristics of each data source. To address this, we propose FRAME, a simple yet effective architecture that combines device pose and camera feeds for state-of-the-art body pose prediction through geometrically sound multimodal integration and can run at 300 FPS on modern hardware. Lastly, we showcase a novel training strategy to enhance the model's generalization capabilities. Our approach exploits the problem's geometric properties, yielding high-quality motion capture free from common artifacts in prior works. Qualitative and quantitative evaluations, along with extensive comparisons, demonstrate the effectiveness of our method. Data, code, and CAD designs will be available at https://vcai.mpi-inf.mpg.de/projects/FRAME/
|
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] |
107 |
Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch
|
[
"Aneeshan Sain",
"Subhajit Maity",
"Pinaki Nath Chowdhury",
"Shubhadeep Koley",
"Ayan Kumar Bhunia",
"Yi-Zhe Song"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Sain_Sketch_Down_the_FLOPs_Towards_Efficient_Networks_for_Human_Sketch_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Sain_Sketch_Down_the_FLOPs_Towards_Efficient_Networks_for_Human_Sketch_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sain_Sketch_Down_the_CVPR_2025_supplemental.zip
|
2505.23763
|
@InProceedings{Sain_2025_CVPR,
author = {Sain, Aneeshan and Maity, Subhajit and Chowdhury, Pinaki Nath and Koley, Shubhadeep and Bhunia, Ayan Kumar and Song, Yi-Zhe},
title = {Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28383-28393}
}
|
As sketch research has collectively matured over time, its adaptation for at-mass commercialisation emerges on the immediate horizon. Despite an already mature research endeavour for photos, there is no research on the efficient inference specifically designed for sketch data. In this paper, we first demonstrate existing state-of-the-art efficient light-weight models designed for photos do not work on sketches. We then propose two sketch-specific components which work in a plug-n-play manner on any photo efficient network to adapt them to work on sketch data. We specifically chose fine-grained sketch-based image retrieval (FG-SBIR) as a demonstrator as the most recognised sketch problem with immediate commercial value. Technically speaking, we first propose a cross-modal knowledge distillation network to transfer existing photo efficient networks to be compatible with sketch, which brings down number of FLOPs and model parameters by 97.96% percent and 84.89% respectively. We then exploit the abstract trait of sketch to introduce a RL-based canvas selector that dynamically adjusts to the abstraction level which further cuts down number of FLOPs by two thirds. The end result is an overall reduction of 99.37% of FLOPs (from 40.18G to 0.254G) when compared with a full network, while retaining the accuracy (33.03% vs 32.77%) -- finally making an efficient network for the sparse sketch data that exhibit even fewer FLOPs than the best photo counterpart.
|
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] |
108 |
Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation
|
[
"Libiao Chen",
"Dong Nie",
"Junjun Pan",
"Jing Yan",
"Zhenyu Tang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Generalized_Zero-Shot_Classification_via_Semantics-Free_Inter-Class_Feature_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Generalized_Zero-Shot_Classification_via_Semantics-Free_Inter-Class_Feature_Generation_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Chen_2025_CVPR,
author = {Chen, Libiao and Nie, Dong and Pan, Junjun and Yan, Jing and Tang, Zhenyu},
title = {Generalized Zero-Shot Classification via Semantics-Free Inter-Class Feature Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20286-20295}
}
|
Generalized Zero-Shot Learning (GZSL) addresses the challenge of classifying unseen classes in the presence of seen classes by leveraging semantic attributes to bridge the gap for unseen classes. However, in image based disease classification, such as glioma sub-typing, distinguishing between classes using image semantic attributes can be challenging. To address this challenge, we introduce a novel GZSL method that eliminates the dependency on semantic information. Specifically, we propose that the primary of most classification in clinic is risk stratification, and classes are inherently ordered rather than purely categorical. Based on this insight, we present an inter-class feature augmentation (IFA) module, where distributions of different classes are ordered by their risk levels in a learned feature space using pre-defined joint conditional Gaussian distribution model. This ordering enables the generation of unseen class features through feature mixing of adjacent seen classes, effectively transforming the zero-shot learning problem into a supervised learning task. Our method eliminates the need for explicit semantic information, avoiding the cross-modal alignment between visual and semantic features. Moreover, the IFA module for GZSL requires no structural modifications to the existing classification models. In the experiment, both in-house and public datasets are used to evaluate our method across different tasks, including glioma subtyping, Alzheimer's disease (AD) classification and diabetic retinopathy classification. Experimental results demonstrate that our method outperforms the state-of-the-art GZSL methods with statistical significance.
|
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] |
109 |
Feat2GS: Probing Visual Foundation Models with Gaussian Splatting
|
[
"Yue Chen",
"Xingyu Chen",
"Anpei Chen",
"Gerard Pons-Moll",
"Yuliang Xiu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Feat2GS_Probing_Visual_Foundation_Models_with_Gaussian_Splatting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Feat2GS_Probing_Visual_Foundation_Models_with_Gaussian_Splatting_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Feat2GS_Probing_Visual_CVPR_2025_supplemental.pdf
|
2412.09606
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Yue and Chen, Xingyu and Chen, Anpei and Pons-Moll, Gerard and Xiu, Yuliang},
title = {Feat2GS: Probing Visual Foundation Models with Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6348-6361}
}
|
Given that visual foundation models (VFMs) are trained on extensive datasets but often limited to 2D images, a natural question arises: how well do they understand the 3D world? With the differences in architecture and training protocols (i.e., objectives, proxy tasks), a unified framework to fairly and comprehensively probe their 3D awareness is urgently needed. Existing works on 3D probing suggest single-view 2.5D estimation (e.g., depth and normal) or two-view sparse 2D correspondence (e.g., matching and tracking). Unfortunately, these tasks ignore texture awareness, and require 3D data as ground-truth, which limits the scale and diversity of their evaluation set. To address these issues, we introduce Feat2GS, which readout 3D Gaussians attributes from VFM features extracted from unposed images. This allows us to probe 3D awareness for geometry and texture via novel view synthesis, without requiring 3D data. Additionally, the disentanglement of 3DGS parameters - geometry (x, a, S) and texture (c) - enables separate analysis of texture and geometry awareness. Under Feat2GS, we conduct extensive experiments to probe the 3D awareness of several VFMs, and investigate the ingredients that lead to a 3D aware VFM. Building on these findings, we develop several variants that achieve state-of-the-art across diverse datasets. This makes Feat2GS useful for probing VFMs, and as a simple-yet-effective baseline for novel-view synthesis. Code and data will be made available at fanegg.github.io/Feat2GS.
|
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] |
110 |
Multi-Modal Aerial-Ground Cross-View Place Recognition with Neural ODEs
|
[
"Sijie Wang",
"Rui She",
"Qiyu Kang",
"Siqi Li",
"Disheng Li",
"Tianyu Geng",
"Shangshu Yu",
"Wee Peng Tay"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Multi-Modal_Aerial-Ground_Cross-View_Place_Recognition_with_Neural_ODEs_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Multi-Modal_Aerial-Ground_Cross-View_Place_Recognition_with_Neural_ODEs_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Multi-Modal_Aerial-Ground_Cross-View_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Sijie and She, Rui and Kang, Qiyu and Li, Siqi and Li, Disheng and Geng, Tianyu and Yu, Shangshu and Tay, Wee Peng},
title = {Multi-Modal Aerial-Ground Cross-View Place Recognition with Neural ODEs},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11717-11728}
}
|
Place recognition (PR) aims at retrieving the query place from a database and plays a crucial role in various applications, including navigation, autonomous driving, and augmented reality. While previous multi-modal PR works have mainly focused on the same-view scenario in which ground-view descriptors are matched with a database of ground-view descriptors during inference, the multi-modal cross-view scenario, in which ground-view descriptors are matched with aerial-view descriptors in a database, remains under-explored. We propose AGPlace, a model that effectively integrates information from multi-modal ground sensors (cameras and LiDARs) to achieve accurate aerial-ground PR. AGPlace achieves effective aerial-ground cross-view PR by leveraging a manifold-based neural ordinary differential equation (ODE) framework with a multi-domain alignment loss. It outperforms existing state-of-the-art cross-view PR models on large-scale datasets. As most existing PR models are designed for ground-ground PR, we adapt these baselines into our cross-view pipeline. Experiments demonstrate that this direct adaptation performs worse than our overall model architecture AGPlace. AGPlace represents a significant advancement in multi-modal aerial-ground PR, with promising implications for real-world applications.
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111 |
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
|
[
"Saad Wazir",
"Daeyoung Kim"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wazir_Rethinking_Decoder_Design_Improving_Biomarker_Segmentation_Using_Depth-to-Space_Restoration_and_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wazir_Rethinking_Decoder_Design_Improving_Biomarker_Segmentation_Using_Depth-to-Space_Restoration_and_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wazir_Rethinking_Decoder_Design_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wazir_2025_CVPR,
author = {Wazir, Saad and Kim, Daeyoung},
title = {Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30861-30871}
}
|
Segmenting biomarkers in medical images is crucial for various biotech applications. Despite advances, Transformer and CNN based methods often struggle with variations in staining and morphology, limiting feature extraction. In medical image segmentation, where datasets often have limited sample availability, recent state-of-the-art (SOTA) methods achieve higher accuracy by leveraging pre-trained encoders, whereas end-to-end methods tend to underperform. This is due to challenges in effectively transferring rich multiscale features from encoders to decoders, as well as limitations in decoder efficiency. To address these issues, we propose an architecture that captures multi-scale local and global contextual information and a novel decoder design, which effectively integrates features from the encoder, emphasizes important channels and regions, and reconstructs spatial dimensions to enhance segmentation accuracy. Our method, compatible with various encoders, outperforms SOTA methods, as demonstrated by experiments on four datasets and ablation studies. Specifically, our method achieves absolute performance gains of 2.76% on MoNuSeg, 3.12% on DSB, 2.87% on Electron Microscopy, and 4.03% on TNBC datasets compared to existing SOTA methods. Code: https://github.com/saadwazir/MCADS-Decoder
|
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] |
112 |
MaDCoW: Marginal Distortion Correction for Wide-Angle Photography with Arbitrary Objects
|
[
"Kevin Zhang",
"Jia-Bin Huang",
"Jose Echevarria",
"Stephen DiVerdi",
"Aaron Hertzmann"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MaDCoW_Marginal_Distortion_Correction_for_Wide-Angle_Photography_with_Arbitrary_Objects_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MaDCoW_Marginal_Distortion_Correction_for_Wide-Angle_Photography_with_Arbitrary_Objects_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MaDCoW_Marginal_Distortion_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Kevin and Huang, Jia-Bin and Echevarria, Jose and DiVerdi, Stephen and Hertzmann, Aaron},
title = {MaDCoW: Marginal Distortion Correction for Wide-Angle Photography with Arbitrary Objects},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10923-10932}
}
|
We introduce MaDCoW, a method for correcting marginal distortion of arbitrary objects in wide-angle photography. People often use wide-angle photography to convey natural scenes--smartphones typically default to wide-angle photography--but depicting very wide-field-of-view scenes produces distorted object appearance, particularly marginal distortion in linear projections. With MaDCoW, a user annotates regions-of-interest to correct, along with straight lines. For each region, MaDCoW solves for a local-linear perspective projection and then jointly solves for a projection for the whole photograph that minimizes distortion. We show that our method can produce good results in cases where previous methods yield visible distortions.
|
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] |
113 |
SynTab-LLaVA: Enhancing Multimodal Table Understanding with Decoupled Synthesis
|
[
"Bangbang Zhou",
"Zuan Gao",
"Zixiao Wang",
"Boqiang Zhang",
"Yuxin Wang",
"Zhineng Chen",
"Hongtao Xie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_SynTab-LLaVA_Enhancing_Multimodal_Table_Understanding_with_Decoupled_Synthesis_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_SynTab-LLaVA_Enhancing_Multimodal_Table_Understanding_with_Decoupled_Synthesis_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_SynTab-LLaVA_Enhancing_Multimodal_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zhou_2025_CVPR,
author = {Zhou, Bangbang and Gao, Zuan and Wang, Zixiao and Zhang, Boqiang and Wang, Yuxin and Chen, Zhineng and Xie, Hongtao},
title = {SynTab-LLaVA: Enhancing Multimodal Table Understanding with Decoupled Synthesis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24796-24806}
}
|
Due to the limited scale of multimodal table understanding (MTU) data, model performance is constrained. A straightforward approach is to use multimodal large language models to obtain more samples, but this may cause hallucinations, generate incorrect sample pairs, and cost significantly.To address the above issues, we design a simple yet effective synthesis framework that consists of two independent steps: table image rendering and table question and answer (Q&A) pairs generation.We use table codes (HTML, LaTeX, Markdown) to synthesize images and generate Q&A pairs with large language model (LLM).This approach leverages LLM's high concurrency and low cost to boost annotation efficiency and reduce expenses. By inputting code instead of images, LLMs can directly access the content and structure of the table, reducing hallucinations in table understanding and improving the accuracy of generated Q&A pairs. Finally, we synthesize a large-scale MTU dataset, SynTab, containing 636K images and 1.8M samples costing within \200 in US dollars. We further introduce a generalist tabular multimodal model, SynTab-LLaVA. This model not only effectively extracts local textual content within the table but also enables global modeling of relationships between cells.SynTab-LLaVA achieves SOTA performance on 21 out of 24 in-domain and out-of-domain benchmarks, demonstrating the effectiveness and generalization of our method. The Code is available at \href https://github.com/bang123-box/SynTab-LLaVA SynTab-LLaVA .
|
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] |
114 |
Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing
|
[
"Hanhui Wang",
"Yihua Zhang",
"Ruizheng Bai",
"Yue Zhao",
"Sijia Liu",
"Zhengzhong Tu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Edit_Away_and_My_Face_Will_not_Stay_Personal_Biometric_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Edit_Away_and_My_Face_Will_not_Stay_Personal_Biometric_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Edit_Away_and_CVPR_2025_supplemental.pdf
|
2411.16832
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Hanhui and Zhang, Yihua and Bai, Ruizheng and Zhao, Yue and Liu, Sijia and Tu, Zhengzhong},
title = {Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23806-23816}
}
|
Recent advancements in diffusion models have made generative image editing more accessible than ever. While these developments allow users to generate creative edits with ease, they also raise significant ethical concerns, particularly regarding malicious edits to human portraits that threaten individuals' privacy and identity security. Existing general-purpose image protection methods primarily focus on generating adversarial perturbations to nullify edit effects. However, these approaches often exhibit instability to protect against diverse editing requests. In this work, we introduce a novel perspective to personal human portrait protection against malicious editing. Unlike traditional methods aiming to prevent edits from taking effect, our method, FaceLock, optimizes adversarial perturbations to ensure that original biometric information---such as facial features---is either destroyed or substantially altered post-editing, rendering the subject in the edited output biometrically unrecognizable. Our approach innovatively integrates facial recognition and visual perception factors into the perturbation optimization process, ensuring robust protection against a variety of editing attempts. Besides, we shed light on several critical issues with commonly used evaluation metrics in image editing and reveal cheating methods by which they can be easily manipulated, leading to deceptive assessments of protection. Through extensive experiments, we demonstrate that FaceLock significantly outperforms all baselines in defense performance against a wide range of malicious edits. Moreover, our method also exhibits strong robustness against purification techniques. Comprehensive ablation studies confirm the stability and broad applicability of our method across diverse diffusion-based editing algorithms. Our work not only advances the state-of-the-art in biometric defense but also sets the foundation for more secure and privacy-preserving practices in image editing.
|
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] |
115 |
Any6D: Model-free 6D Pose Estimation of Novel Objects
|
[
"Taeyeop Lee",
"Bowen Wen",
"Minjun Kang",
"Gyuree Kang",
"In So Kweon",
"Kuk-Jin Yoon"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Any6D_Model-free_6D_Pose_Estimation_of_Novel_Objects_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Any6D_Model-free_6D_Pose_Estimation_of_Novel_Objects_CVPR_2025_paper.pdf
| null |
2503.18673
|
@InProceedings{Lee_2025_CVPR,
author = {Lee, Taeyeop and Wen, Bowen and Kang, Minjun and Kang, Gyuree and Kweon, In So and Yoon, Kuk-Jin},
title = {Any6D: Model-free 6D Pose Estimation of Novel Objects},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11633-11643}
}
|
We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, Toyota-Light, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d
|
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] |
116 |
Improving Accuracy and Calibration via Differentiated Deep Mutual Learning
|
[
"Han Liu",
"Peng Cui",
"Bingning Wang",
"Weipeng Chen",
"Yupeng Zhang",
"Jun Zhu",
"Xiaolin Hu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Improving_Accuracy_and_Calibration_via_Differentiated_Deep_Mutual_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Improving_Accuracy_and_Calibration_via_Differentiated_Deep_Mutual_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Improving_Accuracy_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Han and Cui, Peng and Wang, Bingning and Chen, Weipeng and Zhang, Yupeng and Zhu, Jun and Hu, Xiaolin},
title = {Improving Accuracy and Calibration via Differentiated Deep Mutual Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25812-25821}
}
|
Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, particularly in terms of prediction accuracy. However, in real-world scenarios, especially in safety-critical applications, accuracy alone is insufficient; reliable uncertainty estimates are essential. Modern DNNs, often trained with cross-entropy loss, tend to exhibit overconfidence, especially on ambiguous samples. Many techniques aim to improve uncertainty calibration, yet they often come at the cost of reduced accuracy or increased computational demands. To address this challenge, we propose Differentiated Deep Mutual Learning (Diff-DML), an efficient ensemble approach that simultaneously enhances accuracy and uncertainty calibration. Diff-DML draws inspiration from Deep Mutual Learning (DML) while introducing two strategies to maintain prediction diversity: (1) Differentiated Training Strategy (DTS) and (2) Diversity-Preserving Learning Objective (DPLO). Our theoretical analysis shows that Diff-DML's diversified learning framework not only leverages ensemble benefits but also avoids the loss of prediction diversity observed in traditional DML setups, which is crucial for improved calibration. Extensive evaluations on various benchmarks confirm the effectiveness of Diff-DML. For instance, on the CIFAR-100 dataset, Diff-DML on ResNet34 model achieved substantial improvements over the previous state-of-the-art method, MDCA, with absolute accuracy gains of 1.3%/3.1%, relative ECE reductions of 49.6%/43.8%, and relative classwise-ECE reductions of 7.7%/13.0%.
|
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] |
117 |
DrVideo: Document Retrieval Based Long Video Understanding
|
[
"Ziyu Ma",
"Chenhui Gou",
"Hengcan Shi",
"Bin Sun",
"Shutao Li",
"Hamid Rezatofighi",
"Jianfei Cai"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_DrVideo_Document_Retrieval_Based_Long_Video_Understanding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_DrVideo_Document_Retrieval_Based_Long_Video_Understanding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_DrVideo_Document_Retrieval_CVPR_2025_supplemental.pdf
|
2406.12846
|
@InProceedings{Ma_2025_CVPR,
author = {Ma, Ziyu and Gou, Chenhui and Shi, Hengcan and Sun, Bin and Li, Shutao and Rezatofighi, Hamid and Cai, Jianfei},
title = {DrVideo: Document Retrieval Based Long Video Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18936-18946}
}
|
Most of the existing methods for video understanding primarily focus on videos only lasting tens of seconds, with limited exploration of techniques for handling long videos. The increased number of frames in long videos poses two main challenges: difficulty in locating key information and performing long-range reasoning. Thus, we propose DrVideo, a document-retrieval-based system designed for long video understanding. Our key idea is to convert the long-video understanding problem into a long-document understanding task so as to effectively leverage the power of large language models. Specifically, DrVideo first transforms a long video into a coarse text-based long document to initially retrieve key frames and then updates the documents with the augmented key frame information. It then employs an agent-based iterative loop to continuously search for missing information and augment the document until sufficient question-related information is gathered for making the final predictions in a chain-of-thought manner. Extensive experiments on long video benchmarks confirm the effectiveness of our method. DrVideo significantly outperforms existing LLM-based state-of-the-art methods on EgoSchema benchmark (3 minutes), MovieChat-1K benchmark (10 minutes), and the long split of Video-MME benchmark (average of 44 minutes). Code is available at https://github.com/Upper9527/DrVideo.
|
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] |
118 |
Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
|
[
"Ye Li",
"Yanchao Zhao",
"Chengcheng Zhu",
"Jiale Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Infighting_in_the_Dark_Multi-Label_Backdoor_Attack_in_Federated_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Infighting_in_the_CVPR_2025_supplemental.pdf
|
2409.19601
|
@InProceedings{Li_2025_CVPR,
author = {Li, Ye and Zhao, Yanchao and Zhu, Chengcheng and Zhang, Jiale},
title = {Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25770-25779}
}
|
Federated Learning (FL), a privacy-preserving decentralized machine learning framework, has been shown to be vulnerable to backdoor attacks. Current research primarily focuses on the Single-Label Backdoor Attack (SBA), wherein adversaries share a consistent target. However, a critical fact is overlooked: adversaries may be non-cooperative, have distinct targets, and operate independently, which exhibits a more practical scenario called Multi-Label Backdoor Attack (MBA). Unfortunately, prior works are ineffective in the MBA scenario since non-cooperative attackers exclude each other. In this work, we conduct an in-depth investigation to uncover the inherent constraints of the exclusion: similar backdoor mappings are constructed for different targets, resulting in conflicts among backdoor functions. To address this limitation, we propose Mirage, the first non-cooperative MBA strategy in FL that allows attackers to inject effective and persistent backdoors into the global model without collusion by constructing in-distribution (ID) backdoor mapping. Specifically, we introduce an adversarial adaptation method to bridge the backdoor features and the target distribution in an ID manner. Additionally, we further leverage a constrained optimization method to ensure the ID mapping survives in the global training dynamics. Extensive evaluations demonstrate that Mirage outperforms various state-of-the-art attacks and bypasses existing defenses, achieving an average ASR greater than 97% and maintaining over 90% after 900 rounds. This work aims to alert researchers to this potential threat and inspire the design of effective defense mechanisms. Code has been made open-source.
|
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] |
119 |
Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors
|
[
"Zhengfei Kuang",
"Tianyuan Zhang",
"Kai Zhang",
"Hao Tan",
"Sai Bi",
"Yiwei Hu",
"Zexiang Xu",
"Milos Hasan",
"Gordon Wetzstein",
"Fujun Luan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kuang_Buffer_Anytime_Zero-Shot_Video_Depth_and_Normal_from_Image_Priors_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kuang_Buffer_Anytime_Zero-Shot_Video_Depth_and_Normal_from_Image_Priors_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kuang_Buffer_Anytime_Zero-Shot_CVPR_2025_supplemental.pdf
|
2411.17249
|
@InProceedings{Kuang_2025_CVPR,
author = {Kuang, Zhengfei and Zhang, Tianyuan and Zhang, Kai and Tan, Hao and Bi, Sai and Hu, Yiwei and Xu, Zexiang and Hasan, Milos and Wetzstein, Gordon and Luan, Fujun},
title = {Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17660-17670}
}
|
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and video--normal training data. Instead of relying on large-scale annotated video datasets, we demonstrate high-quality video buffer estimation by leveraging single-image priors with temporal consistency constraints. Our zero-shot training strategy combines state-of-the-art image estimation models based on optical flow smoothness through a hybrid loss function, implemented via a lightweight temporal attention architecture. Applied to leading image models like Depth Anything V2 and Marigold-E2E-FT, our approach significantly improves temporal consistency while maintaining accuracy. Experiments show that our method not only outperforms image-based approaches but also achieves results comparable to state-of-the-art video models trained on large-scale paired video datasets, despite using no such paired video data.
|
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] |
120 |
PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing
|
[
"Peng Li",
"Wangguandong Zheng",
"Yuan Liu",
"Tao Yu",
"Yangguang Li",
"Xingqun Qi",
"Xiaowei Chi",
"Siyu Xia",
"Yan-Pei Cao",
"Wei Xue",
"Wenhan Luo",
"Yike Guo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_PSHuman_Photorealistic_Single-image_3D_Human_Reconstruction_using_Cross-Scale_Multiview_Diffusion_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_PSHuman_Photorealistic_Single-image_3D_Human_Reconstruction_using_Cross-Scale_Multiview_Diffusion_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_PSHuman_Photorealistic_Single-image_CVPR_2025_supplemental.pdf
|
2409.10141
|
@InProceedings{Li_2025_CVPR,
author = {Li, Peng and Zheng, Wangguandong and Liu, Yuan and Yu, Tao and Li, Yangguang and Qi, Xingqun and Chi, Xiaowei and Xia, Siyu and Cao, Yan-Pei and Xue, Wei and Luo, Wenhan and Guo, Yike},
title = {PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16008-16018}
}
|
Photorealistic 3D human modeling is essential for various applications and has seen tremendous progress. However, existing methods for monocular full-body reconstruction, typically relying on front and/or predicted back view, still struggle with satisfactory performance due to the ill-posed nature of the problem and sophisticated self-occlusions. In this paper, we propose PSHuman, a novel framework that explicitly reconstructs human meshes utilizing priors from the multiview diffusion model. It is found that directly applying multiview diffusion on single-view human images leads to severe geometric distortions, especially on generated faces. To address it, we propose a cross-scale diffusion that models the joint probability distribution of global full-body shape and local facial characteristics, enabling identity-preserved novel-view generation without geometric distortion. Moreover, to enhance cross-view body shape consistency of varied human poses, we condition the generative model on parametric models (SMPL-X), which provide body priors and prevent unnatural views inconsistent with human anatomy. Leveraging the generated multiview normal and color images, we present SMPLX-initialized explicit human carving to recover realistic textured human meshes efficiently. Extensive experiments on CAPE and THuman2.1 demonstrate PSHuman's superiority in geometry details, texture fidelity, and generalization capability.
|
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] |
121 |
LSNet: See Large, Focus Small
|
[
"Ao Wang",
"Hui Chen",
"Zijia Lin",
"Jungong Han",
"Guiguang Ding"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LSNet_See_Large_Focus_Small_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LSNet_See_Large_Focus_Small_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LSNet_See_Large_CVPR_2025_supplemental.pdf
|
2503.23135
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Ao and Chen, Hui and Lin, Zijia and Han, Jungong and Ding, Guiguang},
title = {LSNet: See Large, Focus Small},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9718-9729}
}
|
Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations pose challenges for practical deployments, particularly in real-time applications. To tackle this issue, researchers have explored various lightweight and efficient network designs. However, existing lightweight models predominantly leverage self-attention mechanisms and convolutions for token mixing. This dependence brings limitations in effectiveness and efficiency in the perception and aggregation processes of lightweight networks, hindering the balance between performance and efficiency under limited computational budgets. In this paper, we draw inspiration from the dynamic heteroscale vision ability inherent in the efficient human vision system and propose a "See Large, Focus Small" strategy for lightweight vision network design. We introduce LS (Large-Small) convolution, which combines large-kernel perception and small-kernel aggregation. It can efficiently capture a wide range of perceptual information and achieve precise feature aggregation for dynamic and complex visual representations, thus enabling proficient processing of visual information. Based on LS convolution, we present LSNet, a new family of lightweight models. Extensive experiments demonstrate that LSNet achieves superior performance and efficiency over existing lightweight networks in various vision tasks. Codes and models are available at https://github.com/jameslahm/lsnet.
|
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] |
122 |
DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes
|
[
"Jinxiu Liu",
"Shaoheng Lin",
"Yinxiao Li",
"Ming-Hsuan Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DynamicScaler_Seamless_and_Scalable_Video_Generation_for_Panoramic_Scenes_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DynamicScaler_Seamless_and_Scalable_Video_Generation_for_Panoramic_Scenes_CVPR_2025_paper.pdf
| null |
2412.11100
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Jinxiu and Lin, Shaoheng and Li, Yinxiao and Yang, Ming-Hsuan},
title = {DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6144-6153}
}
|
The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360deg panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dynamic content synthesis. In this work, we propose DynamicScaler, addressing these challenges by enabling spatially scalable and panoramic dynamic scene synthesis that preserves coherence across panoramic scenes of arbitrary size. Specifically, we introduce a Offset Shifting Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic dynamic scenes via a diffusion model with fixed resolution through a seamless rotating Window, which ensures seamless boundary transitions and consistency across the entire panoramic space, accommodating varying resolutions and aspect ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure both local detail fidelity and global motion continuity. Extensive experiments demonstrate our method achieves superior content and motion quality in panoramic scene-level video generation, offering a training-free, efficient, and scalable solution for immersive dynamic scene creation with constant VRAM consumption regardless of the output video resolution. Project page is available at https://dynamic-scaler.pages.dev/new.
|
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] |
123 |
Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics
|
[
"Shibo Zhao",
"Sifan Zhou",
"Raphael Blanchard",
"Yuheng Qiu",
"Wenshan Wang",
"Sebastian Scherer"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Tartan_IMU_A_Light_Foundation_Model_for_Inertial_Positioning_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Tartan_IMU_A_Light_Foundation_Model_for_Inertial_Positioning_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Tartan_IMU_A_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Shibo and Zhou, Sifan and Blanchard, Raphael and Qiu, Yuheng and Wang, Wenshan and Scherer, Sebastian},
title = {Tartan IMU: A Light Foundation Model for Inertial Positioning in Robotics},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22520-22529}
}
|
Despite recent advances in deep learning, most existing learning IMU odometry methods are trained on specific datasets, lack generalization, and are prone to overfitting, which limits their real-world application. To address these challenges, we present Tartan IMU, a foundation model designed for generalizable, IMU-based state estimation across diverse robotic platforms. Our approach consists of three-stage: First, a pre-trained foundation model leverages over 100 hours of multi-platform data to establish general motion knowledge, achieving 36% improvement in ATE over specialized models. Second, to adapt to previously unseen tasks, we employ the Low-Rank Adaptation (LoRA), allowing positive transfer with only 1.1 M trainable parameters. Finally, to support robotics deployment, we introduce online test-time adaptation, which eliminates the boundary between training and testing, allowing the model to continuously "learn as it operates" at 200 FPS in real-time.
|
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] |
124 |
Event Ellipsometer: Event-based Mueller-Matrix Video Imaging
|
[
"Ryota Maeda",
"Yunseong Moon",
"Seung-Hwan Baek"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Maeda_Event_Ellipsometer_Event-based_Mueller-Matrix_Video_Imaging_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maeda_Event_Ellipsometer_Event-based_CVPR_2025_supplemental.zip
|
2411.17313
|
@InProceedings{Maeda_2025_CVPR,
author = {Maeda, Ryota and Moon, Yunseong and Baek, Seung-Hwan},
title = {Event Ellipsometer: Event-based Mueller-Matrix Video Imaging},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21804-21813}
}
|
Light-matter interactions modify both the intensity and polarization state of light. Changes in polarization, represented by a Mueller matrix, encode detailed scene information. Existing optical ellipsometers capture Mueller-matrix images; however, they are often limited to static scenes due to long acquisition times. Here, we introduce Event Ellipsometer, a method for acquiring Mueller-matrix images of dynamic scenes. Our imaging system employs fast-rotating quarter-wave plates (QWPs) in front of a light source and an event camera that asynchronously captures intensity changes induced by the rotating QWPs. We develop an ellipsometric-event image formation model, a calibration method, and an ellipsometric-event reconstruction method. We experimentally demonstrate that Event Ellipsometer enables Mueller-matrix imaging at 30fps, extending ellipsometry to dynamic scenes.
|
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] |
125 |
DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding
|
[
"Wenhui Liao",
"Jiapeng Wang",
"Hongliang Li",
"Chengyu Wang",
"Jun Huang",
"Lianwen Jin"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_DocLayLLM_An_Efficient_Multi-modal_Extension_of_Large_Language_Models_for_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_DocLayLLM_An_Efficient_Multi-modal_Extension_of_Large_Language_Models_for_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_DocLayLLM_An_Efficient_CVPR_2025_supplemental.pdf
|
2408.15045
|
@InProceedings{Liao_2025_CVPR,
author = {Liao, Wenhui and Wang, Jiapeng and Li, Hongliang and Wang, Chengyu and Huang, Jun and Jin, Lianwen},
title = {DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4038-4049}
}
|
Text-rich document understanding (TDU) requires comprehensive analysis of documents containing substantial textual content and complex layouts. While Multimodal Large Language Models (MLLMs) have achieved fast progress in this domain, existing approaches either demand significant computational resources or struggle with effective multi-modal integration. In this paper, we introduce DocLayLLM, an efficient multi-modal extension of LLMs specifically designed for TDU. By lightly integrating visual patch tokens and 2D positional tokens into LLMs' input and encoding the document content using the LLMs themselves, we fully take advantage of the document comprehension capability of LLMs and enhance their perception of OCR information. We have also deeply considered the role of chain-of-thought (CoT) and innovatively proposed the techniques of CoT Pre-training and CoT Annealing. Our DocLayLLM can achieve remarkable performances with lightweight training settings, showcasing its efficiency and effectiveness. Experimental results demonstrate that our DocLayLLM outperforms existing OCR-dependent methods and OCR-free competitors. Code and model are available at https://github.com/whlscut/DocLayLLM.
|
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] |
126 |
EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation
|
[
"Zihao Zhang",
"Haoran Chen",
"Haoyu Zhao",
"Guansong Lu",
"Yanwei Fu",
"Hang Xu",
"Zuxuan Wu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_EDEN_Enhanced_Diffusion_for_High-quality_Large-motion_Video_Frame_Interpolation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_EDEN_Enhanced_Diffusion_for_High-quality_Large-motion_Video_Frame_Interpolation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_EDEN_Enhanced_Diffusion_CVPR_2025_supplemental.pdf
|
2503.15831
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Zihao and Chen, Haoran and Zhao, Haoyu and Lu, Guansong and Fu, Yanwei and Xu, Hang and Wu, Zuxuan},
title = {EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2105-2115}
}
|
Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over traditional optical flow-based approaches, they still struggle to generate sharp, temporally consistent frames in scenarios with large motion. To address this limitation, we introduce EDEN, an Enhanced Diffusion for high-quality large-motion vidEo frame iNterpolation. Our approach first utilizes a transformer-based tokenizer to produce refined latent representations of the intermediate frames for diffusion models. We then enhance the diffusion transformer with temporal attention across the process and incorporate a start-end frame difference embedding to guide the generation of dynamic motion. Extensive experiments demonstrate that EDEN achieves state-of-the-art results across popular benchmarks, including nearly a 10% LPIPS reduction on DAVIS and SNU-FILM, and an 8% improvement on DAIN-HD.
|
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] |
127 |
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
|
[
"Hao Yu",
"Xin Yang",
"Le Zhang",
"Hanlin Gu",
"Tianrui Li",
"Lixin Fan",
"Qiang Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Handling_Spatial-Temporal_Data_Heterogeneity_for_Federated_Continual_Learning_via_Tail_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Handling_Spatial-Temporal_Data_CVPR_2025_supplemental.pdf
|
2412.18355
|
@InProceedings{Yu_2025_CVPR,
author = {Yu, Hao and Yang, Xin and Zhang, Le and Gu, Hanlin and Li, Tianrui and Fan, Lixin and Yang, Qiang},
title = {Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4874-4883}
}
|
Federated Continual Learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not only spatial data heterogeneity between clients but also temporal data heterogeneity between tasks. In this paper, empirical experiments demonstrate that such input-level heterogeneity significantly affects the model's internal parameters and outputs, leading to severe spatial-temporal catastrophic forgetting of local and previous knowledge. To this end, we propose Federated Tail Anchor (FedTA) to mix trainable Tail Anchor with the frozen output features to adjust their position in the feature space, thereby overcoming parameter-forgetting and output-forgetting. Three novel components are also included: Input Enhancement for improving the performance of pre-trained models on downstream tasks; Selective Input Knowledge Fusion for fusion of heterogeneous local knowledge on the server; and Best Global Prototype Selection for finding the best anchor point for each class in the feature space. Extensive experiments demonstrate that FedTA not only outperforms existing FCL methods but also effectively preserves the relative positions of features.
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] |
128 |
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
|
[
"Chensheng Peng",
"Chengwei Zhang",
"Yixiao Wang",
"Chenfeng Xu",
"Yichen Xie",
"Wenzhao Zheng",
"Kurt Keutzer",
"Masayoshi Tomizuka",
"Wei Zhan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_DeSiRe-GS_4D_Street_Gaussians_for_Static-Dynamic_Decomposition_and_Surface_Reconstruction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_DeSiRe-GS_4D_Street_Gaussians_for_Static-Dynamic_Decomposition_and_Surface_Reconstruction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_DeSiRe-GS_4D_Street_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Peng_2025_CVPR,
author = {Peng, Chensheng and Zhang, Chengwei and Wang, Yixiao and Xu, Chenfeng and Xie, Yichen and Zheng, Wenzhao and Keutzer, Kurt and Tomizuka, Masayoshi and Zhan, Wei},
title = {DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6782-6791}
}
|
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios. Our approach employs a two-stage optimization pipeline of dynamic street Gaussians. In the first stage, we extract 2D motion masks based on the observation that 3D Gaussian Splatting inherently can reconstruct only the static regions in dynamic environments. These extracted 2D motion priors are then mapped into the Gaussian space in a differentiable manner, leveraging an efficient formulation of dynamic Gaussians in the second stage. Combined with the introduced geometric regularizations, our method are able to address the over-fitting issues caused by data sparsity in autonomous driving, reconstructing physically plausible Gaussians that align with object surfaces rather than floating in air. Furthermore, we introduce temporal cross-view consistency to ensure coherence across time and viewpoints, resulting in high-quality surface reconstruction. Comprehensive experiments demonstrate the efficiency and effectiveness of DeSiRe-GS, surpassing prior self-supervised arts and achieving accuracy comparable to methods relying on external 3D bounding box annotations.
|
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] |
129 |
End-to-End HOI Reconstruction Transformer with Graph-based Encoding
|
[
"Zhenrong Wang",
"Qi Zheng",
"Sihan Ma",
"Maosheng Ye",
"Yibing Zhan",
"Dongjiang Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_End-to-End_HOI_Reconstruction_Transformer_with_Graph-based_Encoding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_End-to-End_HOI_Reconstruction_Transformer_with_Graph-based_Encoding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_End-to-End_HOI_Reconstruction_CVPR_2025_supplemental.pdf
|
2503.06012
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Zhenrong and Zheng, Qi and Ma, Sihan and Ye, Maosheng and Zhan, Yibing and Li, Dongjiang},
title = {End-to-End HOI Reconstruction Transformer with Graph-based Encoding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27706-27715}
}
|
Human-object interaction (HOI) reconstruction has garnered significant attention due to its diverse applications and the success of capturing human meshes. Existing HOI reconstruction methods often rely on explicitly modeling interactions between humans and objects. However, such a way leads to a natural conflict between 3D mesh reconstruction, which emphasizes global structure, and fine-grained contact reconstruction, which focuses on local details. To address the limitations of explicit modeling, we propose the End-to-End HOI Reconstruction Transformer with Graph-based Encoding (HOI-TG). It implicitly learns the interaction between humans and objects by leveraging self-attention mechanisms. Within the transformer architecture, we devise graph residual blocks to aggregate the topology among vertices of different spatial structures. This dual focus effectively balances global and local representations. Without bells and whistles, HOI-TG achieves state-of-the-art performance on BEHAVE and InterCap datasets. Particularly on the challenging InterCap dataset, our method improves the reconstruction results for human and object meshes by 8.9% and 8.6%, respectively.
|
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] |
130 |
REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning
|
[
"Jihyun Lee",
"Weipeng Xu",
"Alexander Richard",
"Shih-En Wei",
"Shunsuke Saito",
"Shaojie Bai",
"Te-Li Wang",
"Minhyuk Sung",
"Tae-Kyun Kim",
"Jason Saragih"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_REWIND_Real-Time_Egocentric_Whole-Body_Motion_Diffusion_with_Exemplar-Based_Identity_Conditioning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_REWIND_Real-Time_Egocentric_Whole-Body_Motion_Diffusion_with_Exemplar-Based_Identity_Conditioning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_REWIND_Real-Time_Egocentric_CVPR_2025_supplemental.zip
|
2504.04956
|
@InProceedings{Lee_2025_CVPR,
author = {Lee, Jihyun and Xu, Weipeng and Richard, Alexander and Wei, Shih-En and Saito, Shunsuke and Bai, Shaojie and Wang, Te-Li and Sung, Minhyuk and Kim, Tae-Kyun and Saragih, Jason},
title = {REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7095-7104}
}
|
We present REWIND (Real-Time Egocentric Whole-Body Motion Diffusion), a one-step diffusion model for real-time, high-fidelity human motion estimation from egocentric image inputs. While an existing method for egocentric whole-body (i.e., body and hands) motion estimation is non-real-time and acausal due to diffusion-based iterative motion refinement to capture correlations between body and hand poses, REWIND operates in a fully causal and real-time manner. To enable real-time inference, we introduce (1) cascaded body-hand denoising diffusion, which effectively models the correlation between egocentric body and hand motions in a fast, feed-forward manner, and (2) diffusion distillation, which enables high-quality motion estimation with a single denoising step. Our denoising diffusion model is based on a modified Transformer architecture, designed to causally model output motions while enhancing generalizability to unseen motion lengths. Additionally, REWIND optionally supports identity-conditioned motion estimation when identity prior is available. To this end, we propose a novel identity conditioning method based on a small set of pose exemplars of the target identity, which further enhances motion estimation quality. Through extensive experiments, we demonstrate that REWIND significantly outperforms the existing baselines both with and without exemplar-based identity conditioning.
|
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] |
131 |
Hiding Images in Diffusion Models by Editing Learned Score Functions
|
[
"Haoyu Chen",
"Yunqiao Yang",
"Nan Zhong",
"Kede Ma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Hiding_Images_in_Diffusion_Models_by_Editing_Learned_Score_Functions_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Hiding_Images_in_Diffusion_Models_by_Editing_Learned_Score_Functions_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Hiding_Images_in_CVPR_2025_supplemental.pdf
|
2503.18459
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Haoyu and Yang, Yunqiao and Zhong, Nan and Ma, Kede},
title = {Hiding Images in Diffusion Models by Editing Learned Score Functions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18663-18673}
}
|
Hiding data using neural networks (i.e., neural steganography) has achieved remarkable success across both discriminative classifiers and generative adversarial networks. However, the potential of data hiding in diffusion models remains relatively unexplored. Current methods exhibit limitations in achieving high extraction accuracy, model fidelity, and hiding efficiency due primarily to the entanglement of the hiding and extraction processes with multiple denoising diffusion steps. To address these, we describe a simple yet effective approach that embeds images at specific timesteps in the reverse diffusion process by editing the learned score functions. Additionally, we introduce a parameter-efficient fine-tuning method that combines gradient-based parameter selection with low-rank adaptation to enhance model fidelity and hiding efficiency. Comprehensive experiments demonstrate that our method extracts high-quality images at human-indistinguishable levels, replicates the original model behaviors at both sample and population levels, and embeds images orders of magnitude faster than prior methods. Besides, our method naturally supports multi-recipient scenarios through independent extraction channels.
|
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] |
132 |
Disco4D: Disentangled 4D Human Generation and Animation from a Single Image
|
[
"Hui En Pang",
"Shuai Liu",
"Zhongang Cai",
"Lei Yang",
"Tianwei Zhang",
"Ziwei Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_Disco4D_Disentangled_4D_Human_Generation_and_Animation_from_a_Single_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_Disco4D_Disentangled_4D_Human_Generation_and_Animation_from_a_Single_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pang_Disco4D_Disentangled_4D_CVPR_2025_supplemental.zip
|
2409.17280
|
@InProceedings{Pang_2025_CVPR,
author = {Pang, Hui En and Liu, Shuai and Cai, Zhongang and Yang, Lei and Zhang, Tianwei and Liu, Ziwei},
title = {Disco4D: Disentangled 4D Human Generation and Animation from a Single Image},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26331-26344}
}
|
We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancing the generation details and flexibility. It has the following technical innovations. (1) Disco4D learns to efficiently fit the clothing Gaussians over the SMPL-X Gaussians. (2) It adopts diffusion models to enhance the 3D generation process, e.g. modeling occluded parts not visible in the input image. (3) It learns an identity encoding for each clothing Gaussian to facilitate the separation and extraction of clothing assets. Furthermore, Disco4D naturally supports 4D human animation with vivid dynamics. Extensive experiments demonstrate the superiority of Disco4D on 4D human generation and animation tasks.
|
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] |
133 |
DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles
|
[
"Rui Zhao",
"Weijia Mao",
"Mike Zheng Shou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DoraCycle_Domain-Oriented_Adaptation_of_Unified_Generative_Model_in_Multimodal_Cycles_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DoraCycle_Domain-Oriented_Adaptation_of_Unified_Generative_Model_in_Multimodal_Cycles_CVPR_2025_paper.pdf
| null |
2503.03651
|
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Rui and Mao, Weijia and Shou, Mike Zheng},
title = {DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2835-2846}
}
|
Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging, especially when these domains require substantial paired data to capture the targeted distributions. Since unpaired data from a single modality, such as vision or language, is more readily available, we utilize the bidirectional mappings between vision and language learned by the unified generative model to enable training on unpaired data for domain adaptation. Specifically, we propose DoraCycle, which integrates two multimodal cycles: text-to-image-to-text and image-to-text-to-image. The model is optimized through cross-entropy loss computed at the cycle endpoints, where both endpoints share the same modality. This facilitates self-evolution of the model without reliance on annotated text-image pairs. Experimental results demonstrate that for tasks independent of paired knowledge, such as stylization, DoraCycle can effectively adapt the unified model using only unpaired data. For tasks involving new paired knowledge, such as specific identities, a combination of a small set of paired image-text examples and larger-scale unpaired data is sufficient for effective domain-oriented adaptation. The code will be released at https://github.com/showlab/DoraCycle.
|
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-0.01968461275100708,
-0.016255531460046768,
-0.009315788745880127,
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0.054799504578113556,
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] |
134 |
WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion
|
[
"Yang Wu",
"Yun Zhu",
"Kaihua Zhang",
"Jianjun Qian",
"Jin Xie",
"Jian Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_WeatherGen_A_Unified_Diverse_Weather_Generator_for_LiDAR_Point_Clouds_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_WeatherGen_A_Unified_Diverse_Weather_Generator_for_LiDAR_Point_Clouds_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_WeatherGen_A_Unified_CVPR_2025_supplemental.pdf
|
2504.13561
|
@InProceedings{Wu_2025_CVPR,
author = {Wu, Yang and Zhu, Yun and Zhang, Kaihua and Qian, Jianjun and Xie, Jin and Yang, Jian},
title = {WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17019-17028}
}
|
3D scene perception demands a large amount of adverse-weather LiDAR data, yet the cost of LiDAR data collection presents a significant scaling-up challenge. To this end, a series of LiDAR simulators have been proposed. Yet, they can only simulate a single adverse weather with a single physical model, and the fidelity is quite limited. This paper presents **WeatherGen**, the first unified diverse-weather LiDAR data diffusion generation framework, significantly improving fidelity. Specifically, we first design a map-based data producer, which is capable of providing a vast amount of high-quality diverse-weather data for training purposes. Then, we utilize the diffusion-denoising paradigm to construct a diffusion model. Among them, we propose a spider mamba generator with the spider mamba scan to restore the disturbed diverse weather data gradually. The spider mamba models the feature interactions by scanning the LiDAR beam circle and central ray, excellently maintaining the physical structure of the LiDAR point cloud. Subsequently, we design a latent domain aligner following the generator to transfer real-world knowledge. Afterward, we devise a contrastive learning-based controller, which equips weather control signals with compact semantic knowledge through language supervision from CLIP, guiding the diffusion model in generating more discriminative data. Finally, we fine-tune WeatherGen with small-scale real-world data to further enhance its performance. Extensive evaluations on KITTI-360 and Seeing Through Fog demonstrate the high generation quality of WeatherGen. Through WeatherGen, we construct the mini-weather dataset, promoting the performance of the downstream task under adverse weather conditions.
|
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] |
135 |
MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking
|
[
"Haolin Qin",
"Tingfa Xu",
"Tianhao Li",
"Zhenxiang Chen",
"Tao Feng",
"Jianan Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_MUST_The_First_Dataset_and_Unified_Framework_for_Multispectral_UAV_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_MUST_The_First_Dataset_and_Unified_Framework_for_Multispectral_UAV_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_MUST_The_First_CVPR_2025_supplemental.pdf
|
2503.17699
|
@InProceedings{Qin_2025_CVPR,
author = {Qin, Haolin and Xu, Tingfa and Li, Tianhao and Chen, Zhenxiang and Feng, Tao and Li, Jianan},
title = {MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16882-16891}
}
|
UAV tracking faces significant challenges in real-world scenarios, such as small-size targets and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this field has been hindered by the lack of relevant datasets. To address this gap, we introduce the first large-scale Multispectral UAV Single Object Tracking dataset (MUST), which includes 250 video sequences spanning diverse environments and challenges, providing a comprehensive data foundation for multispectral UAV tracking. We also propose a novel tracking framework, UNTrack, which encodes unified spectral, spatial, and temporal features from spectrum prompts, initial templates, and sequential searches. UNTrack employs an asymmetric transformer with a spectral background eliminate mechanism for optimal relationship modeling and an encoder that continuously updates the spectrum prompt to refine tracking, improving both accuracy and efficiency. Extensive experiments show that our proposed UNTrack outperforms state-of-the-art UAV trackers. We believe our dataset and framework will drive future research in this area. The dataset is available on https://github.com/q2479036243/MUST-Multispectral-UAV-Single-Object-Tracking.
|
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] |
136 |
IDOL: Instant Photorealistic 3D Human Creation from a Single Image
|
[
"Yiyu Zhuang",
"Jiaxi Lv",
"Hao Wen",
"Qing Shuai",
"Ailing Zeng",
"Hao Zhu",
"Shifeng Chen",
"Yujiu Yang",
"Xun Cao",
"Wei Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_IDOL_Instant_Photorealistic_3D_Human_Creation_from_a_Single_Image_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_IDOL_Instant_Photorealistic_3D_Human_Creation_from_a_Single_Image_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_IDOL_Instant_Photorealistic_CVPR_2025_supplemental.zip
|
2412.14963
|
@InProceedings{Zhuang_2025_CVPR,
author = {Zhuang, Yiyu and Lv, Jiaxi and Wen, Hao and Shuai, Qing and Zeng, Ailing and Zhu, Hao and Chen, Shifeng and Yang, Yujiu and Cao, Xun and Liu, Wei},
title = {IDOL: Instant Photorealistic 3D Human Creation from a Single Image},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26308-26319}
}
|
Creating a high-fidelity, animatable 3D full-body avatar from a single image is a challenging task due to the diverse appearance and poses of humans and the limited availability of high-quality training data. To achieve fast and high-quality human reconstruction, this work rethinks the task from the perspectives of dataset, model, and representation. First, we introduce a large-scale HUman GEnerated training dataset, HuGe100K, consisting of 100K diverse, photorealistic human images with corresponding 24-view in a static pose or dynamic pose frames generated via a pose-controllable image-to-video model. Next, leveraging the diversity in views, poses, and appearances within HuGe100K, we develop a scalable feed-forward transformer model to predict a 3D human Gaussian representation in a uniform space of a given human image. This model is trained to disentangle human pose, shape, clothing geometry, and texture. Accordingly, the estimated Gaussians can be animated robustly without post-processing. We conduct comprehensive experiments to validate the effectiveness of the proposed dataset and method. Our model demonstrates the generalizable ability to efficiently reconstruct photorealistic humans in under 1 second using a single GPU. Additionally, it seamlessly supports various applications, including animation, shape, and texture editing tasks.
|
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] |
137 |
Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone
|
[
"Yuan Xiao",
"Yuchen Chen",
"Shiqing Ma",
"Chunrong Fang",
"Tongtong Bai",
"Mingzheng Gu",
"Yuxin Cheng",
"Yanwei Chen",
"Zhenyu Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Tightening_Robustness_Verification_of_MaxPool-based_Neural_Networks_via_Minimizing_the_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Tightening_Robustness_Verification_of_MaxPool-based_Neural_Networks_via_Minimizing_the_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Tightening_Robustness_Verification_CVPR_2025_supplemental.pdf
|
2211.09810
|
@InProceedings{Xiao_2025_CVPR,
author = {Xiao, Yuan and Chen, Yuchen and Ma, Shiqing and Fang, Chunrong and Bai, Tongtong and Gu, Mingzheng and Cheng, Yuxin and Chen, Yanwei and Chen, Zhenyu},
title = {Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20695-20705}
}
|
The robustness of neural network classifiers is important in the safety-critical domain and can be quantified by robustness verification. At present, efficient and scalable verification techniques are always sound but incomplete, and thus, the improvement of verified robustness results is the key criterion to evaluate the performance of incomplete verification approaches. The multi-variate function MaxPool is widely adopted yet challenging to verify. In this paper, we present Ti-Lin, a robustness verifier for MaxPool-based CNNs with Tight Linear Approximation. Following the sequel of minimizing the over-approximation zone of the non-linear function of CNNs, we are the first to propose the provably neuron-wise tightest linear bounds for the MaxPool function. By our proposed linear bounds, we can certify larger robustness results for CNNs. We evaluate the effectiveness of Ti-Lin on different verification frameworks with open-sourced benchmarks, including LeNet, PointNet, and networks trained on the MNIST, CIFAR-10, Tiny ImageNet and ModelNet40 datasets. Experimental results show that Ti-Lin significantly outperforms the state-of-the-art methods across all networks with up to 78.6% improvement in terms of the certified accuracy with almost the same time consumption as the fastest tool. Our code is available at https://anonymous.4open.science/r/Ti-Lin-cvpr-72EE.
|
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] |
138 |
SketchVideo: Sketch-based Video Generation and Editing
|
[
"Feng-Lin Liu",
"Hongbo Fu",
"Xintao Wang",
"Weicai Ye",
"Pengfei Wan",
"Di Zhang",
"Lin Gao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SketchVideo_Sketch-based_Video_Generation_and_Editing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SketchVideo_Sketch-based_Video_Generation_and_Editing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SketchVideo_Sketch-based_Video_CVPR_2025_supplemental.zip
|
2503.23284
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Feng-Lin and Fu, Hongbo and Wang, Xintao and Ye, Weicai and Wan, Pengfei and Zhang, Di and Gao, Lin},
title = {SketchVideo: Sketch-based Video Generation and Editing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23379-23390}
}
|
Video generation and editing conditioned on text prompts or images have undergone significant advancements. However, challenges remain in accurately controlling global layout and geometry details solely by texts, and supporting motion control and local modification through images. In this paper, we aim to achieve sketch-based spatial and motion control for video generation and support fine-grained editing of real or synthetic videos. Based on the DiT video generation model, we propose a memory-efficient control structure with sketch control blocks that predict residual features of skipped DiT blocks. Sketches are drawn on one or two keyframes (at arbitrary time points) for easy interaction. To propagate such temporally sparse sketch conditions across all frames, we propose an inter-frame attention mechanism to analyze the relationship between the keyframes and each video frame. For sketch-based video editing, we design an additional video insertion module that maintains consistency between the newly edited content and the original video's spatial feature and dynamic motion. During inference, we use latent fusion for the accurate preservation of unedited regions. Extensive experiments demonstrate that our SketchVideo achieves superior performance in controllable video generation and editing.
|
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] |
139 |
PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
|
[
"Martin Spitznagel",
"Jan Vaillant",
"Janis Keuper"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Spitznagel_PhysicsGen_Can_Generative_Models_Learn_from_Images_to_Predict_Complex_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Spitznagel_PhysicsGen_Can_Generative_Models_Learn_from_Images_to_Predict_Complex_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Spitznagel_PhysicsGen_Can_Generative_CVPR_2025_supplemental.pdf
|
2503.05333
|
@InProceedings{Spitznagel_2025_CVPR,
author = {Spitznagel, Martin and Vaillant, Jan and Keuper, Janis},
title = {PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11125-11134}
}
|
The image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the context of physical simulations. Providing a dataset of 300k image-pairs and baseline evaluations for three different physical simulation tasks, we propose a benchmark to investigate the following research questions: i) are generative models able to learn complex physical relations from input-output image pairs? ii) what speedups can be achieved by replacing differential equation based simulations? While baseline evaluations of different current models show the potential for high speedups (ii), these results also show strong limitations toward the physical correctness (i). This underlines the need for new methods to enforce physical correctness.
|
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] |
140 |
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
|
[
"Maochen Yang",
"Zekun Li",
"Jian Zhang",
"Lei Qi",
"Yinghuan Shi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Taste_More_Taste_Better_Diverse_Data_and_Strong_Model_Boost_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Taste_More_Taste_Better_Diverse_Data_and_Strong_Model_Boost_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Taste_More_Taste_CVPR_2025_supplemental.pdf
|
2503.17984
|
@InProceedings{Yang_2025_CVPR,
author = {Yang, Maochen and Li, Zekun and Zhang, Jian and Qi, Lei and Shi, Yinghuan},
title = {Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24440-24451}
}
|
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.
|
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] |
141 |
Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering
|
[
"Yutao Feng",
"Xiang Feng",
"Yintong Shang",
"Ying Jiang",
"Chang Yu",
"Zeshun Zong",
"Tianjia Shao",
"Hongzhi Wu",
"Kun Zhou",
"Chenfanfu Jiang",
"Yin Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Gaussian_Splashing_Unified_Particles_for_Versatile_Motion_Synthesis_and_Rendering_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Gaussian_Splashing_Unified_Particles_for_Versatile_Motion_Synthesis_and_Rendering_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_Gaussian_Splashing_Unified_CVPR_2025_supplemental.zip
|
2401.15318
|
@InProceedings{Feng_2025_CVPR,
author = {Feng, Yutao and Feng, Xiang and Shang, Yintong and Jiang, Ying and Yu, Chang and Zong, Zeshun and Shao, Tianjia and Wu, Hongzhi and Zhou, Kun and Jiang, Chenfanfu and Yang, Yin},
title = {Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {518-529}
}
|
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS. Leveraging the coherence of the Gaussian Splatting and Position-Based Dynamics (PBD) in the underlying representation, we manage rendering, view synthesis, and the dynamics of solids and fluids in a cohesive manner. Similar to GaussianShader, we enhance each Gaussian kernel with an added normal, aligning the kernel's orientation with the surface normal to refine the PBD simulation. This approach effectively eliminates spiky noises that arise from rotational deformation in solids. It also allows us to integrate physically based rendering to augment the dynamic surface reflections on fluids. Consequently, our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views.
|
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] |
142 |
Improve Representation for Imbalanced Regression through Geometric Constraints
|
[
"Zijian Dong",
"Yilei Wu",
"Chongyao Chen",
"Yingtian Zou",
"Yichi Zhang",
"Juan Helen Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Improve_Representation_for_Imbalanced_Regression_through_Geometric_Constraints_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Improve_Representation_for_Imbalanced_Regression_through_Geometric_Constraints_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Improve_Representation_for_CVPR_2025_supplemental.pdf
|
2503.00876
|
@InProceedings{Dong_2025_CVPR,
author = {Dong, Zijian and Wu, Yilei and Chen, Chongyao and Zou, Yingtian and Zhang, Yichi and Zhou, Juan Helen},
title = {Improve Representation for Imbalanced Regression through Geometric Constraints},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5082-5091}
}
|
In representation learning, uniformity refers to the uniform feature distribution in the latent space (i.e., unit hypersphere). Previous work has shown that improving uniformity contributes to the learning of under-represented classes. However, most of the previous work focused on classification; the representation space of imbalanced regression remains unexplored. Classification-based methods are not suitable for regression tasks because they cluster features into distinct groups without considering the continuous and ordered nature essential for regression. In a geometric aspect, we uniquely focus on ensuring uniformity in the latent space for imbalanced regression through two key losses: enveloping and homogeneity. The enveloping loss encourages the induced trace to uniformly occupy the surface of a hypersphere, while the homogeneity loss ensures smoothness, with representations evenly spaced at consistent intervals. Our method integrates these geometric principles into the data representations via a Surrogate-driven Representation Learning (SRL) framework. Experiments with real-world regression and operator learning tasks highlight the importance of uniformity in imbalanced regression and validate the efficacy of our geometry-based loss functions.
|
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] |
143 |
AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models
|
[
"Xinghui Li",
"Qichao Sun",
"Pengze Zhang",
"Fulong Ye",
"Zhichao Liao",
"Wanquan Feng",
"Songtao Zhao",
"Qian He"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_AnyDressing_Customizable_Multi-Garment_Virtual_Dressing_via_Latent_Diffusion_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_AnyDressing_Customizable_Multi-Garment_Virtual_Dressing_via_Latent_Diffusion_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_AnyDressing_Customizable_Multi-Garment_CVPR_2025_supplemental.pdf
|
2412.04146
|
@InProceedings{Li_2025_CVPR,
author = {Li, Xinghui and Sun, Qichao and Zhang, Pengze and Ye, Fulong and Liao, Zhichao and Feng, Wanquan and Zhao, Songtao and He, Qian},
title = {AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23723-23733}
}
|
Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios. In this paper, we focus on a new task, i.e., Multi-Garment Virtual Dressing, and we propose a novel AnyDressing method for customizing characters conditioned on any combination of garments and any personalized text prompts. AnyDressing primarily comprises two primary networks named GarmentsNet and DressingNet, which are respectively dedicated to extracting detailed clothing features and generating customized images. Specifically, we propose an efficient and scalable module called Garment-Specific Feature Extractor in GarmentsNet to individually encode garment textures in parallel. This design prevents garment confusion while ensuring network efficiency. Meanwhile, we design an adaptive Dressing-Attention mechanism and a novel Instance-Level Garment Localization Learning strategy in DressingNet to accurately inject multi-garment features into their corresponding regions. This approach efficiently integrates multi-garment texture cues into generated images and further enhances text-image consistency. Additionally, we introduce a Garment-Enhanced Texture Learning strategy to improve the fine-grained texture details of garments. Thanks to our well-craft design, AnyDressing can serve as a plug-in module to easily integrate with any community control extensions for diffusion models, improving the diversity and controllability of synthesized images. Extensive experiments show that AnyDressing achieves state-of-the-art results.
|
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0.005523722153156996
] |
144 |
Spectral Informed Mamba for Robust Point Cloud Processing
|
[
"Ali Bahri",
"Moslem Yazdanpanah",
"Mehrdad Noori",
"Sahar Dastani",
"Milad Cheraghalikhani",
"Gustavo Adolfo Vargas Hakim",
"David Osowiechi",
"Farzad Beizaee",
"Ismail Ben Ayed",
"Christian Desrosiers"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Bahri_Spectral_Informed_Mamba_for_Robust_Point_Cloud_Processing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Bahri_Spectral_Informed_Mamba_for_Robust_Point_Cloud_Processing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bahri_Spectral_Informed_Mamba_CVPR_2025_supplemental.pdf
|
2503.04953
|
@InProceedings{Bahri_2025_CVPR,
author = {Bahri, Ali and Yazdanpanah, Moslem and Noori, Mehrdad and Dastani, Sahar and Cheraghalikhani, Milad and Hakim, Gustavo Adolfo Vargas and Osowiechi, David and Beizaee, Farzad and Ben Ayed, Ismail and Desrosiers, Christian},
title = {Spectral Informed Mamba for Robust Point Cloud Processing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11799-11809}
}
|
State Space Models (SSMs) have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba and Masked Autoencoder (MAE) networks for point cloud data in both supervised and self-supervised learning. We propose three key contributions to enhance Mamba's capability in processing complex point cloud structures. First, we exploit the spectrum of a graph Laplacian to capture patch connectivity, defining an isometry-invariant traversal order that is robust to viewpoints and better captures shape manifolds than traditional 3D grid-based traversals. Second, we adapt segmentation via a recursive patch partitioning strategy informed by Laplacian spectral components, allowing finer integration and segment analysis. Third, we address token placement in MAE for Mamba by restoring tokens to their original positions, which preserves essential order and improves learning. Extensive experiments demonstrate our approach's improvements in classification, segmentation, and few-shot tasks over state-of-the-art (SOTA) baselines.
|
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] |
145 |
Latent Space Imaging
|
[
"Matheus Souza",
"Yidan Zheng",
"Kaizhang Kang",
"Yogeshwar Nath Mishra",
"Qiang Fu",
"Wolfgang Heidrich"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Souza_Latent_Space_Imaging_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Souza_Latent_Space_Imaging_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Souza_Latent_Space_Imaging_CVPR_2025_supplemental.pdf
|
2407.07052
|
@InProceedings{Souza_2025_CVPR,
author = {Souza, Matheus and Zheng, Yidan and Kang, Kaizhang and Mishra, Yogeshwar Nath and Fu, Qiang and Heidrich, Wolfgang},
title = {Latent Space Imaging},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28295-28305}
}
|
Digital imaging systems have traditionally relied on brute-force measurement and processing of pixels arranged on regular grids. In contrast, the human visual system performs significant data reduction from the large number of photoreceptors to the optic nerve, effectively encoding visual information into a low-bandwidth latent space representation optimized for brain processing. Inspired by this, we propose a similar approach to advance artificial vision systems. Latent Space Imaging introduces a new paradigm that combines optics and software to encode image information directly into the semantically rich latent space of a generative model. This approach substantially reduces bandwidth and memory demands during image capture and enables a range of downstream tasks focused on the latent space.We validate this principle through an initial hardware prototype based on a single-pixel camera. By implementing an amplitude modulation scheme that encodes into the generative model's latent space, we achieve compression ratios ranging from 1:100 to 1:1000 during imaging, and up to 1:16384 for downstream applications. This approach leverages the model's intrinsic linear boundaries, demonstrating the potential of latent space imaging for highly efficient imaging hardware, adaptable future applications in high-speed imaging, and task-specific cameras with significantly reduced hardware complexity.
|
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] |
146 |
Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization
|
[
"Xiran Wang",
"Jian Zhang",
"Lei Qi",
"Yinghuan Shi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Balanced_Direction_from_Multifarious_Choices_Arithmetic_Meta-Learning_for_Domain_Generalization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Balanced_Direction_from_Multifarious_Choices_Arithmetic_Meta-Learning_for_Domain_Generalization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Balanced_Direction_from_CVPR_2025_supplemental.pdf
|
2503.18987
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Xiran and Zhang, Jian and Qi, Lei and Shi, Yinghuan},
title = {Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30577-30587}
}
|
Domain generalization is proposed to address distribution shift, arising from statistical disparities between training source and unseen target domains. The widely used first-order meta-learning algorithms demonstrate strong performance for domain generalization by leveraging the gradient matching theory, which aims to establish balanced parameters across source domains to reduce overfitting to any particular domain. However, our analysis reveals that there are actually numerous directions to achieve gradient matching, with current methods representing just one possible path. These methods actually overlook another critical factor that the balanced parameters should be close to the centroid of optimal parameters of each source domain. To address this, we propose a simple yet effective arithmetic meta-learning with arithmetic-weighted gradients. This approach, while adhering to the principles of gradient matching, promotes a more precise balance by estimating the centroid between domain-specific optimal parameters. Experimental results validate the effectiveness of our strategy. Our code is available at https://github.com/zzwdx/ARITH.
|
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] |
147 |
Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion Model
|
[
"Yejee Shin",
"Yeeun Lee",
"Hanbyol Jang",
"Geonhui Son",
"Hyeongyu Kim",
"Dosik Hwang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Anatomical_Consistency_and_Adaptive_Prior-informed_Transformation_for_Multi-contrast_MR_Image_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Anatomical_Consistency_and_Adaptive_Prior-informed_Transformation_for_Multi-contrast_MR_Image_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shin_Anatomical_Consistency_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Shin_2025_CVPR,
author = {Shin, Yejee and Lee, Yeeun and Jang, Hanbyol and Son, Geonhui and Kim, Hyeongyu and Hwang, Dosik},
title = {Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30918-30927}
}
|
Multi-contrast magnetic resonance (MR) images offer critical diagnostic information but are limited by long scan times and high cost. While diffusion models (DMs) excel in medical image synthesis, they often struggle to maintain anatomical consistency and utilize the diverse characteristics of multi-contrast MR images effectively. We propose APT, a unified diffusion model designed to generate accurate and anatomically consistent multi-contrast MR images. APT introduces a mutual information fusion module and an anatomical consistency loss to preserve critical anatomical structures across multiple contrast inputs. To enhance synthesis, APT incorporates a two-stage inference process: in the first stage, a prior codebook provides coarse anatomical structures by selecting appropriate guidance based on precomputed similarity mappings and Bezier curve transformations. The second stage applies iterative unrolling with weighted averaging to refine the initial output, enhancing fine anatomical details and ensuring structural consistency. This approach enables the preservation of both global structures and local details, resulting in realistic and diagnostically valuable synthesized images. Extensive experiments on public multi-contrast MR brain images demonstrate that our approach significantly outperforms state-of-the-art methods. The source codes are available at https://github.com/yejees/APT.
|
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] |
148 |
BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations
|
[
"Weixi Feng",
"Chao Liu",
"Sifei Liu",
"William Yang Wang",
"Arash Vahdat",
"Weili Nie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_BlobGEN-Vid_Compositional_Text-to-Video_Generation_with_Blob_Video_Representations_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_BlobGEN-Vid_Compositional_Text-to-Video_Generation_with_Blob_Video_Representations_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_BlobGEN-Vid_Compositional_Text-to-Video_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Feng_2025_CVPR,
author = {Feng, Weixi and Liu, Chao and Liu, Sifei and Wang, William Yang and Vahdat, Arash and Nie, Weili},
title = {BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12989-12998}
}
|
Existing video generation models struggle to follow complex text prompts and synthesize multiple objects, raising the need for additional grounding input for improved controllability. In this work, we propose to decompose videos into visual primitives -- blob video representation, a general representation for controllable video generation. Based on blob conditions, we develop a blob-grounded video diffusion model named BlobGEN-Vid that allows users to control object motions and fine-grained object appearance. In particular, we introduce a masked 3D attention module that effectively improves regional consistency across frames. In addition, we introduce a learnable module to interpolate text embeddings so that users can control semantics in specific frames and obtain smooth object transitions. We show that our framework is model-agnostic and build BlobGEN-Vid based on both U-Net and DiT-based video diffusion models. Extensive experimental results show that BlobGEN-Vid achieves superior zero-shot video generation ability and state-of-the-art layout controllability on multiple benchmarks. When combined with a Large Language Model for layout planning, our framework even outperforms proprietary text-to-video generators regarding compositional accuracy.
|
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] |
149 |
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.
|
[
"Haoran Wang",
"Xinji Mai",
"Zeng Tao",
"Xuan Tong",
"Junxiong Lin",
"Yan Wang",
"Jiawen Yu",
"Shaoqi Yan",
"Ziheng Zhou",
"Wenqiang Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_D2SP_Dynamic_Dual-Stage_Purification_Framework_for_Dual_Noise_Mitigation_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_D2SP_Dynamic_Dual-Stage_Purification_Framework_for_Dual_Noise_Mitigation_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_D2SP_Dynamic_Dual-Stage_CVPR_2025_supplemental.pdf
|
2406.16473
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Haoran and Mai, Xinji and Tao, Zeng and Tong, Xuan and Lin, Junxiong and Wang, Yan and Yu, Jiawen and Yan, Shaoqi and Zhou, Ziheng and Zhang, Wenqiang},
title = {D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19218-19229}
}
|
The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We identified two types of noise: one is caused by low-quality data resulting from factors such as occlusion, dim lighting, and blurriness; the other arises from mislabeled data due to annotation bias by annotators. Addressing the two types of noise, we have meticulously crafted a Dynamic Dual-Stage Purification (D2SP) Framework. This initiative aims to dynamically purify the DFER datasets of these two types of noise, ensuring that only high-quality and correctly labeled data is used in the training process. To mitigate low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which computes sample weights and prunes those low-weight samples. After CGP, the Fine-Grained Correction (FGC) stage evaluates prediction stability to correct mislabeled data. Moreover, D2SP is conceived as a general and plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methods. Extensive experiments covering prevalent DFER datasets and deploying multiple benchmark methods have substantiated D2SP's ability to significantly enhance performance metrics.
|
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] |
150 |
PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model
|
[
"Mingju Gao",
"Yike Pan",
"Huan-ang Gao",
"Zongzheng Zhang",
"Wenyi Li",
"Hao Dong",
"Hao Tang",
"Li Yi",
"Hao Zhao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_PartRM_Modeling_Part-Level_Dynamics_with_Large_Cross-State_Reconstruction_Model_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_PartRM_Modeling_Part-Level_Dynamics_with_Large_Cross-State_Reconstruction_Model_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_PartRM_Modeling_Part-Level_CVPR_2025_supplemental.zip
|
2503.19913
|
@InProceedings{Gao_2025_CVPR,
author = {Gao, Mingju and Pan, Yike and Gao, Huan-ang and Zhang, Zongzheng and Li, Wenyi and Dong, Hao and Tang, Hao and Yi, Li and Zhao, Hao},
title = {PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7004-7014}
}
|
As interest grows in world models that predict future states from current observations and actions, accurately modeling part-level dynamics has become increasingly relevant for various applications. Existing approaches, such as Puppet-Master, rely on fine-tuning large-scale pre-trained video diffusion models, which are impractical for real-world use due to the limitations of 2D video representation and slow processing times. To overcome these challenges, we present PartRM, a novel 4D reconstruction framework that simultaneously models appearance, geometry, and part-level motion from multi-view images of a static object. PartRM builds upon large 3D Gaussian reconstruction models, leveraging their extensive knowledge of appearance and geometry in static objects. To address data scarcity in 4D, we introduce the PartDrag-4D dataset, providing multi-view observations of part-level dynamics across over 20,000 states. We enhance the model's understanding of interaction conditions with a multi-scale drag embedding module that captures dynamics at varying granularities. To prevent catastrophic forgetting during fine-tuning, we implement a two-stage training process that focuses sequentially on motion and appearance learning. Experimental results show that PartRM establishes a new state-of-the-art in part-level motion learning and can be applied in manipulation tasks in robotics. Our code, data, and models are publicly available to facilitate future research.
|
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] |
151 |
LaVin-DiT: Large Vision Diffusion Transformer
|
[
"Zhaoqing Wang",
"Xiaobo Xia",
"Runnan Chen",
"Dongdong Yu",
"Changhu Wang",
"Mingming Gong",
"Tongliang Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LaVin-DiT_Large_Vision_Diffusion_Transformer_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LaVin-DiT_Large_Vision_Diffusion_Transformer_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LaVin-DiT_Large_Vision_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Zhaoqing and Xia, Xiaobo and Chen, Runnan and Yu, Dongdong and Wang, Changhu and Gong, Mingming and Liu, Tongliang},
title = {LaVin-DiT: Large Vision Diffusion Transformer},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20060-20070}
}
|
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natural language processing architectures, which rely on less efficient autoregressive techniques and disrupt spatial relationships essential for vision data, LaVin-DiT introduces key innovations to optimize generative performance for vision tasks. First, to address the high dimensionality of visual data, we incorporate a spatial-temporal variational autoencoder that encodes data into a continuous latent space. Second, for generative modeling, we develop a joint diffusion transformer that progressively produces vision outputs. Third, for unified multi-task training, in-context learning is implemented. Input-target pairs serve as task context, which guides the diffusion transformer to align outputs with specific tasks within the latent space. During inference, a task-specific context set and test data as queries allow LaVin-DiT to generalize across tasks without fine-tuning. Trained on extensive vision datasets, the model is scaled from 0.1B to 3.4B parameters, demonstrating substantial scalability and state-of-the-art performance across diverse vision tasks. This work introduces a novel pathway for large vision foundation models, underscoring the promising potential of diffusion transformers. The code and models are available at https://derrickwang005.github.io/LaVin-DiT.
|
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] |
152 |
DiffFNO: Diffusion Fourier Neural Operator
|
[
"Xiaoyi Liu",
"Hao Tang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DiffFNO_Diffusion_Fourier_Neural_Operator_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DiffFNO_Diffusion_Fourier_Neural_Operator_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_DiffFNO_Diffusion_Fourier_CVPR_2025_supplemental.pdf
|
2411.09911
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Xiaoyi and Tang, Hao},
title = {DiffFNO: Diffusion Fourier Neural Operator},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {150-160}
}
|
We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO). Mode Rebalancing in WFNO effectively captures critical frequency components, significantly improving the reconstruction of high-frequency image details that are crucial for super-resolution tasks. Gated Fusion Mechanism (GFM) adaptively complements WFNO's spectral features with spatial features from an Attention-based Neural Operator (AttnNO). This enhances the network's capability to capture both global structures and local details. Adaptive Time-Step (ATS) ODE solver, a deterministic sampling strategy, accelerates inference without sacrificing output quality by dynamically adjusting integration step sizes ATS. Extensive experiments demonstrate that DiffFNO achieves state-of-the-art (SOTA) results, outperforming existing methods across various scaling factors by a margin of 2-4 dB in PSNR, including those beyond the training distribution. It also achieves this at lower inference time. Our approach sets a new standard in super-resolution, delivering both superior accuracy and computational efficiency.
|
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] |
153 |
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image
|
[
"Jingshun Huang",
"Haitao Lin",
"Tianyu Wang",
"Yanwei Fu",
"Xiangyang Xue",
"Yi Zhu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_CAP-Net_A_Unified_Network_for_6D_Pose_and_Size_Estimation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_CAP-Net_A_Unified_Network_for_6D_Pose_and_Size_Estimation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_CAP-Net_A_Unified_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Huang_2025_CVPR,
author = {Huang, Jingshun and Lin, Haitao and Wang, Tianyu and Fu, Yanwei and Xue, Xiangyang and Zhu, Yi},
title = {CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11654-11664}
}
|
This paper tackles category-level pose estimation of ar- ticulated objects in robotic manipulation tasks and intro- duces a new benchmark dataset. While recent methods es- timate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation for 6D poses. These approaches overlook dense semantic cues from RGB images, leading to suboptimal accuracy, partic- ularly for objects with small parts. To address these limita- tions, we propose a single-stage Network, CAP-Net, for es- timating the 6D poses and sizes of Categorical Articulated Parts. This method combines RGB-D features to generate instance segmentation and NPCS representations for each part in an end-to-end manner. CAP-Net uses a unified net- work to simultaneously predict point-wise class labels, cen- troid offsets, and NPCS maps. A clustering algorithm then groups points of the same predicted class based on their es- timated centroid distances to isolate each part. Finally, the NPCS region of each part is aligned with the point cloud to recover its final pose and size. To bridge the sim-to-real do- main gap, we introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors. Experimental evaluations on the RGBD-Art dataset demon- strate that our method significantly outperforms the state- of-the-art approach. Real-world deployments of our model in robotic tasks underscore its robustness and exceptional sim-to-real transfer capabilities, confirming its substantial practical utility.
|
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] |
154 |
SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks
|
[
"Shining Wang",
"Yunlong Wang",
"Ruiqi Wu",
"Bingliang Jiao",
"Wenxuan Wang",
"Peng Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SeCap_Self-Calibrating_and_Adaptive_Prompts_for_Cross-view_Person_Re-Identification_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SeCap_Self-Calibrating_and_Adaptive_Prompts_for_Cross-view_Person_Re-Identification_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SeCap_Self-Calibrating_and_CVPR_2025_supplemental.pdf
|
2503.06965
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Shining and Wang, Yunlong and Wu, Ruiqi and Jiao, Bingliang and Wang, Wenxuan and Wang, Peng},
title = {SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22119-22128}
}
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When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we face the main challenge of the significant appearance variations caused by different viewpoints, making identity matching difficult. To address this issue, previous methods attempt to reduce the differences between viewpoints by critical attributes and decoupling the viewpoints. While these methods can mitigate viewpoint differences to some extent, they still face two main issues: (1) difficulty in handling viewpoint diversity and (2) neglect of the contribution of local features. To effectively address these challenges, we design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method for the AGPReID task. The core of this framework relies on the Prompt Re-calibration Module (PRM), which adaptively re-calibrates prompts based on the input. Combined with the Local Feature Refinement Module (LFRM), SeCap can extract view-invariant features from local features for AGPReID. Meanwhile, given the current scarcity of datasets in the AGPReID field, we further contribute two real-world Large-scale Aerial-Ground Person Re-Identification datasets, LAGPeR and G2APS-ReID. The former is collected and annotated by us independently, covering 4,231 unique identities and containing 63,841 high-quality images; the latter is reconstructed from the person search dataset G2APS. Through extensive experiments on AGPReID datasets, we demonstrate that SeCap is a feasible and effective solution for the AGPReID task.
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] |
155 |
Zero-Shot Styled Text Image Generation, but Make It Autoregressive
|
[
"Vittorio Pippi",
"Fabio Quattrini",
"Silvia Cascianelli",
"Alessio Tonioni",
"Rita Cucchiara"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Pippi_Zero-Shot_Styled_Text_Image_Generation_but_Make_It_Autoregressive_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Pippi_Zero-Shot_Styled_Text_Image_Generation_but_Make_It_Autoregressive_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pippi_Zero-Shot_Styled_Text_CVPR_2025_supplemental.pdf
|
2503.17074
|
@InProceedings{Pippi_2025_CVPR,
author = {Pippi, Vittorio and Quattrini, Fabio and Cascianelli, Silvia and Tonioni, Alessio and Cucchiara, Rita},
title = {Zero-Shot Styled Text Image Generation, but Make It Autoregressive},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7910-7919}
}
|
Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either GAN- or diffusion-based, that achieved promising results. Nonetheless, these strategies fail to generalize to novel styles and have technical constraints, particularly in terms of maximum output length and training efficiency. To overcome these limitations, in this work, we propose a novel framework for text-image generation, dubbed Emuru. Our approach leverages a powerful text-image representation model (a variational autoencoder) combined with an autoregressive Transformer. Our approach enables the generation of styled text images conditioned on textual content and style examples, such as specific fonts or handwriting styles. We train our model solely on a diverse, synthetic dataset of English text rendered in over 100,000 typewritten and calligraphy fonts, which gives it the capability to reproduce unseen styles (both fonts and users' handwriting) in zero-shot. To the best of our knowledge, Emuru is the first autoregressive model for HTG, and the first designed specifically for generalization to novel styles. Moreover, our model generates images without background artifacts, which are easier to use for downstream applications. Extensive evaluation on both typewritten and handwritten, any-length text image generation scenarios demonstrates the effectiveness of our approach.
|
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] |
156 |
Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
|
[
"Ziying Song",
"Caiyan Jia",
"Lin Liu",
"Hongyu Pan",
"Yongchang Zhang",
"Junming Wang",
"Xingyu Zhang",
"Shaoqing Xu",
"Lei Yang",
"Yadan Luo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Song_Dont_Shake_the_Wheel_Momentum-Aware_Planning_in_End-to-End_Autonomous_Driving_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Dont_Shake_the_Wheel_Momentum-Aware_Planning_in_End-to-End_Autonomous_Driving_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Dont_Shake_the_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Song_2025_CVPR,
author = {Song, Ziying and Jia, Caiyan and Liu, Lin and Pan, Hongyu and Zhang, Yongchang and Wang, Junming and Zhang, Xingyu and Xu, Shaoqing and Yang, Lei and Luo, Yadan},
title = {Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22432-22441}
}
|
End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control and vulnerability to occlusions in single-frame perception. To address this, we propose the Momentum-Aware Driving (MomAD) framework, which introduces trajectory momentum and perception momentum to stabilize and refine trajectory predictions. MomAD comprises two core components: (1) Topological Trajectory Matching (TTM) employs Hausdorff Distance to select the optimal planning query that aligns with prior paths to ensure coherence; (2) Momentum Planning Interactor (MPI) cross-attends the selected planning query with historical queries to expand static and dynamic perception files. This enriched query, in turn, helps regenerate long-horizon trajectory and reduce collision risks. To mitigate noise arising from dynamic environments and detection errors, we introduce robust instance denoising during training, enabling the planning model to focus on critical signals and improve its robustness. We also propose a novel Trajectory Prediction Consistency (TPC) metric to quantitatively assess planning stability. Experiments on the nuScenes dataset demonstrate that MomAD achieves superior long-term consistency (>3s) compared to SOTA methods. Moreover, evaluations on the curated Turning-nuScenes shows that MomAD reduces the collision rate by 26% and improves TPC by 0.97m (33.45%) over a 6s prediction horizon, while closed-loop on Bench2Drive demonstrates an up to 16.3% improvement in success rate.
|
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] |
157 |
Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection
|
[
"Wenxi Chen",
"Raymond A. Yeh",
"Shaoshuai Mou",
"Yan Gu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Leveraging_Perturbation_Robustness_to_Enhance_Out-of-Distribution_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Leveraging_Perturbation_Robustness_to_Enhance_Out-of-Distribution_Detection_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Leveraging_Perturbation_Robustness_CVPR_2025_supplemental.pdf
|
2503.18784
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Wenxi and Yeh, Raymond A. and Mou, Shaoshuai and Gu, Yan},
title = {Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4724-4733}
}
|
Out-of-distribution (OOD) detection is the task of identifying inputs that deviate from the training data distribution. This capability is essential for the safe deployment of deep computer vision models in open-world environments. In this work, we propose a post-hoc method, Perturbation-Rectified OOD detection (PRO), based on the insight that prediction confidence for OOD inputs is more susceptible to reduction under perturbation than IND inputs. From this observation, we proposed a meta-score function that searches for local minimum scores near original inputs by applying gradient descent. This procedure enhances the separability between in-distribution (IND) and OOD samples. Importantly, the approach improves OOD detection performance without complex modifications to the underlying model architectures or training protocol. To validate our approach, we conduct extensive experiments using the OpenOOD benchmark. Our approach further pushes the limit of softmax-based OOD detection and is the leading post-hoc method for small-scale models. On a CIFAR-10 model with adversarial training, PRO effectively detects near-OOD inputs, achieving a reduction of more than 10% on FPR@95 compared to state-of-the-art methods.
|
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] |
158 |
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning
|
[
"Chenjie Hao",
"Weyl Lu",
"Yifan Xu",
"Yubei Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_Neural_Motion_Simulator_Pushing_the_Limit_of_World_Models_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_Neural_Motion_Simulator_Pushing_the_Limit_of_World_Models_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_Neural_Motion_Simulator_CVPR_2025_supplemental.pdf
|
2504.07095
|
@InProceedings{Hao_2025_CVPR,
author = {Hao, Chenjie and Lu, Weyl and Xu, Yifan and Chen, Yubei},
title = {Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27608-27617}
}
|
An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predicts the future physical state of an embodied system based on current observations and actions. MoSim achieves state-of-the-art performance in physical state prediction and provides competitive performance across a range of downstream tasks. This works shows that when a world model is accurate enough and performs precise long-horizon predictions, it can facilitate efficient skill acquisition in imagined worlds and even enable zero-shot reinforcement learning. Furthermore, MoSim can transform any model-free reinforcement learning (RL) algorithm into a model-based approach, effectively decoupling physical environment modeling from RL algorithm development. This separation allows for independent advancements in RL algorithms and world modeling, significantly improving sample efficiency and enhancing generalization capabilities. Our findings highlight that world models for motion dynamics is a promising direction for developing more versatile and capable embodied systems.
|
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] |
159 |
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization
|
[
"Zhanhao Liang",
"Yuhui Yuan",
"Shuyang Gu",
"Bohan Chen",
"Tiankai Hang",
"Mingxi Cheng",
"Ji Li",
"Liang Zheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Aesthetic_Post-Training_Diffusion_Models_from_Generic_Preferences_with_Step-by-step_Preference_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Aesthetic_Post-Training_Diffusion_Models_from_Generic_Preferences_with_Step-by-step_Preference_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Aesthetic_Post-Training_Diffusion_CVPR_2025_supplemental.pdf
|
2406.04314
|
@InProceedings{Liang_2025_CVPR,
author = {Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},
title = {Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13199-13208}
}
|
Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), which has been applied to diffusion models to improve general image quality including prompt alignment and aesthetics. Popular DPO methods propagate preference labels from clean image pairs to all the intermediate steps along the two generation trajectories. However, preference labels provided in existing datasets are blended with layout and aesthetic opinions, which would disagree with aesthetic preference. Even if aesthetic labels were provided (at substantial cost), it would be hard for the two-trajectory methods to capture nuanced visual differences at different steps. To improve aesthetics economically, this paper uses existing generic preference data and introduces step-by-step preference optimization (SPO) that discards the propagation strategy and allows fine-grained image details to be assessed. Specifically, at each denoising step, we 1) sample a pool of candidates by denoising from a shared noise latent, 2) use a step-aware preference model to find a suitable win-lose pair to supervise the diffusion model, and 3) randomly select one from the pool to initialize the next denoising step. This strategy ensures that diffusion models focus on the subtle, fine-grained visual differences instead of layout aspect. We find that aesthetics can be significantly enhanced by accumulating these improved minor differences. When fine-tuning Stable Diffusion v1.5 and SDXL, SPO yields significant improvements in aesthetics compared with existing DPO methods while not sacrificing image-text alignment compared with vanilla models. Moreover, SPO converges much faster than DPO methods due to the use of more correct preference labels provided by the step-aware preference model. Code and models are available at https://github.com/RockeyCoss/SPO.
|
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] |
160 |
Adversarial Diffusion Compression for Real-World Image Super-Resolution
|
[
"Bin Chen",
"Gehui Li",
"Rongyuan Wu",
"Xindong Zhang",
"Jie Chen",
"Jian Zhang",
"Lei Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Adversarial_Diffusion_Compression_for_Real-World_Image_Super-Resolution_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Adversarial_Diffusion_Compression_for_Real-World_Image_Super-Resolution_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Adversarial_Diffusion_Compression_CVPR_2025_supplemental.pdf
|
2411.13383
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Bin and Li, Gehui and Wu, Rongyuan and Zhang, Xindong and Chen, Jie and Zhang, Jian and Zhang, Lei},
title = {Adversarial Diffusion Compression for Real-World Image Super-Resolution},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28208-28220}
}
|
Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusion (SD)-based Real-ISR methods have achieved remarkable success, their slow, multi-step inference hinders practical deployment. Recent SD-based one-step networks like OSEDiff and S3Diff alleviate this issue but still incur high computational costs due to their reliance on large pretrained SD models. This paper proposes a novel Real-ISR method, AdcSR, by distilling the one-step diffusion network OSEDiff into a streamlined diffusion-GAN model under our Adversarial Diffusion Compression (ADC) framework. We meticulously examine the modules of OSEDiff, categorizing them into two types: (1) Removable (VAE encoder, prompt extractor, text encoder, etc.) and (2) Prunable (denoising UNet and VAE decoder). Since direct removal and pruning can degrade the model's generation capability, we pretrain our pruned VAE decoder to restore its ability to decode images and employ adversarial distillation to compensate for performance loss. This ADC-based diffusion-GAN hybrid design effectively reduces complexity by 73% in inference time, 78% in computation, and 74% in parameters, while preserving the model's generation capability. Experiments manifest that our proposed AdcSR achieves competitive recovery quality on both synthetic and real-world datasets, offering up to 9.3x speedup over previous one-step diffusion-based methods. Code and models are available at https://github.com/Guaishou74851/AdcSR.
|
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] |
161 |
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
|
[
"Utkarsh Mall",
"Cheng Perng Phoo",
"Mia Chiquier",
"Bharath Hariharan",
"Kavita Bala",
"Carl Vondrick"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Mall_DiSciPLE_Learning_Interpretable_Programs_for_Scientific_Visual_Discovery_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Mall_DiSciPLE_Learning_Interpretable_Programs_for_Scientific_Visual_Discovery_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mall_DiSciPLE_Learning_Interpretable_CVPR_2025_supplemental.pdf
|
2502.10060
|
@InProceedings{Mall_2025_CVPR,
author = {Mall, Utkarsh and Phoo, Cheng Perng and Chiquier, Mia and Hariharan, Bharath and Bala, Kavita and Vondrick, Carl},
title = {DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29258-29267}
}
|
Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to make accurate predictions but also to understand the underlying mechanisms for those predictions. Good interpretation is important in scientific workflows, as it allows for better decision-making by providing insights into the data. This paper introduces an automatic way of obtaining such interpretable-by-design models, by learning programs that interleave neural networks. We propose DiSciPLE (Discovering Scientific Programs using LLMs and Evolution) an evolutionary algorithm that leverages common sense and prior knowledge of large language models (LLMs) to create Python programs explaining visual data. Additionally, we propose two improvements: a program critic and a program simplifier to improve our method further to synthesize good programs. On three different real world problems, DiSciPLE learns state-of-the-art programs on novel tasks with no prior literature. For example, we can learn programs with 35% lower error than the closest non-interpretable baseline for population density estimation.
|
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] |
162 |
SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters
|
[
"Jianping Jiang",
"Weiye Xiao",
"Zhengyu Lin",
"Huaizhong Zhang",
"Tianxiang Ren",
"Yang Gao",
"Zhiqian Lin",
"Zhongang Cai",
"Lei Yang",
"Ziwei Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_SOLAMI_Social_Vision-Language-Action_Modeling_for_Immersive_Interaction_with_3D_Autonomous_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_SOLAMI_Social_Vision-Language-Action_Modeling_for_Immersive_Interaction_with_3D_Autonomous_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_SOLAMI_Social_Vision-Language-Action_CVPR_2025_supplemental.pdf
|
2412.00174
|
@InProceedings{Jiang_2025_CVPR,
author = {Jiang, Jianping and Xiao, Weiye and Lin, Zhengyu and Zhang, Huaizhong and Ren, Tianxiang and Gao, Yang and Lin, Zhiqian and Cai, Zhongang and Yang, Lei and Liu, Ziwei},
title = {SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26887-26898}
}
|
Human beings are social animals. How to equip 3D autonomous characters with similar social intelligence that can perceive, understand and interact with humans remains an open yet foundamental problem. In this paper, we introduce SOLAMI, the first end-to-end Social vision-Language-Action (VLA) Modeling framework for Immersive interaction with 3D autonomous characters. Specifically, SOLAMI builds 3D autonomous characters from three aspects: 1) Social VLA Architecture: We propose a unified social VLA framework to generate multimodal response (speech and motion) based on the user's multimodal input to drive the character for social interaction. 2) Interactive Multimodal Data: We present SynMSI, a synthetic multimodal social interaction dataset generated by an automatic pipeline using only existing motion datasets to address the issue of data scarcity. 3) Immersive VR Interface: We develop a VR interface that enables users to immersively interact with these characters driven by various architectures. Extensive quantitative experiments and user studies demonstrate that our framework leads to more precise and natural character responses (in both speech and motion) that align with user expectations with lower latency.
|
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] |
163 |
EntropyMark: Towards More Harmless Backdoor Watermark via Entropy-based Constraint for Open-source Dataset Copyright Protection
|
[
"Ming Sun",
"Rui Wang",
"Zixuan Zhu",
"Lihua Jing",
"Yuanfang Guo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_EntropyMark_Towards_More_Harmless_Backdoor_Watermark_via_Entropy-based_Constraint_for_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_EntropyMark_Towards_More_Harmless_Backdoor_Watermark_via_Entropy-based_Constraint_for_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_EntropyMark_Towards_More_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Sun_2025_CVPR,
author = {Sun, Ming and Wang, Rui and Zhu, Zixuan and Jing, Lihua and Guo, Yuanfang},
title = {EntropyMark: Towards More Harmless Backdoor Watermark via Entropy-based Constraint for Open-source Dataset Copyright Protection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30692-30701}
}
|
High-quality open-source datasets are essential for advancing deep neural networks. However, the unauthorized commercial use of these datasets has raised significant concerns about copyright protection. One promising approach is backdoor watermark-based dataset ownership verification (BW-DOV), in which dataset protectors implant specific backdoors into illicit models through dataset watermarking, enabling the tracing of these models through abnormal prediction behaviors. Unfortunately, the targeted nature of these BW-DOV methods can be maliciously exploited, potentially leading to harmful side effects. While existing harmless methods attempt to mitigate these risks, watermarked datasets can still negatively affect prediction results, partially compromising dataset functionality. In this paper, we propose a more harmless backdoor watermark, called EntropyMark, which improves prediction confidence without altering the final prediction results. For this purpose, an entropy-based constraint is introduced to regulate the probability distribution. Specifically, we design an iterative clean-label dataset watermarking framework. Our framework employs gradient matching and adaptive data selection to optimize backdoor injection. In parallel, we introduce a hypothesis test method grounded in entropy inconsistency to verify dataset ownership. Extensive experiments on benchmark datasets demonstrate the effectiveness, transferability, and defense resistance of our approach.
|
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] |
164 |
Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
|
[
"Han Xiao",
"Yina Xie",
"Guanxin Tan",
"Yinghao Chen",
"Rui Hu",
"Ke Wang",
"Aojun Zhou",
"Hao Li",
"Hao Shao",
"Xudong Lu",
"Peng Gao",
"Yafei Wen",
"Xiaoxin Chen",
"Shuai Ren",
"Hongsheng Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Adaptive_Markup_Language_Generation_for_Contextually-Grounded_Visual_Document_Understanding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Adaptive_Markup_Language_Generation_for_Contextually-Grounded_Visual_Document_Understanding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Adaptive_Markup_Language_CVPR_2025_supplemental.pdf
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2505.05446
|
@InProceedings{Xiao_2025_CVPR,
author = {Xiao, Han and Xie, Yina and Tan, Guanxin and Chen, Yinghao and Hu, Rui and Wang, Ke and Zhou, Aojun and Li, Hao and Shao, Hao and Lu, Xudong and Gao, Peng and Wen, Yafei and Chen, Xiaoxin and Ren, Shuai and Li, Hongsheng},
title = {Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29558-29568}
}
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Visual Document Understanding has become essential with the increase of text-rich visual content. This field poses significant challenges due to the need for effective integration of visual perception and textual comprehension, particularly across diverse document types with complex layouts. Moreover, existing fine-tuning datasets for this domain often fall short in providing the detailed contextual information for robust understanding, leading to hallucinations and limited comprehension of spatial relationships among visual elements. To address these challenges, we propose an innovative pipeline that utilizes adaptive generation of markup languages, such as Markdown, JSON, HTML, and TiKZ, to build highly structured document representations and deliver contextually-grounded responses. We introduce two fine-grained structured datasets: DocMark-Pile, comprising approximately 3.8M pretraining data pairs for document parsing, and DocMark-Instruct, featuring 624k fine-tuning data annotations for grounded instruction following.Extensive experiments demonstrate that our proposed model significantly outperforms existing state-of-the-art MLLMs across a range of visual document understanding benchmarks, facilitating advanced reasoning and comprehension capabilities in complex visual scenarios.
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] |
165 |
BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
|
[
"Yiren Lu",
"Yunlai Zhou",
"Disheng Liu",
"Tuo Liang",
"Yu Yin"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_BARD-GS_Blur-Aware_Reconstruction_of_Dynamic_Scenes_via_Gaussian_Splatting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_BARD-GS_Blur-Aware_Reconstruction_of_Dynamic_Scenes_via_Gaussian_Splatting_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Lu_2025_CVPR,
author = {Lu, Yiren and Zhou, Yunlai and Liu, Disheng and Liang, Tuo and Yin, Yu},
title = {BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16532-16542}
}
|
3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the quality of reconstructions depends heavily on high-quality input images and precise camera poses, which is not that trivial to fulfill in the real-world scenarios. Capturing dynamic scenes with handheld monocular cameras, for instance, typically involves simultaneous movement of both the camera and objects within a single exposure. This combined motion frequently results in image blur that existing methods cannot adequately handle. To address these challenges, we introduce BARD-GS, a novel approach for robust dynamic scene reconstruction that effectively handles blurry inputs and imprecise camera poses. Our method comprises two main components: 1) camera motion deblurring and 2) object motion deblurring. By explicitly decomposing motion blur into camera motion blur and object motion blur and modeling them separately, we achieve significantly improved rendering results in dynamic regions. In addition, we collect a real-world motion blur dataset of dynamic scenes to evaluate our approach. Extensive experiments demonstrate that BARD-GS effectively reconstructs high-quality dynamic scenes under realistic conditions, significantly outperforming existing methods.
|
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] |
166 |
SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing
|
[
"Seokhyeon Hong",
"Chaelin Kim",
"Serin Yoon",
"Junghyun Nam",
"Sihun Cha",
"Junyong Noh"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_SALAD_Skeleton-aware_Latent_Diffusion_for_Text-driven_Motion_Generation_and_Editing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_SALAD_Skeleton-aware_Latent_Diffusion_for_Text-driven_Motion_Generation_and_Editing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_SALAD_Skeleton-aware_Latent_CVPR_2025_supplemental.pdf
|
2503.13836
|
@InProceedings{Hong_2025_CVPR,
author = {Hong, Seokhyeon and Kim, Chaelin and Yoon, Serin and Nam, Junghyun and Cha, Sihun and Noh, Junyong},
title = {SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7158-7168}
}
|
Text-driven motion generation has advanced significantly with the rise of denoising diffusion models. However, previous methods often oversimplify representations for the skeletal joints, temporal frames, and textual words, limiting their ability to fully capture the information within each modality and their interactions. Moreover, when using pre-trained models for downstream tasks, such as editing, they typically require additional efforts, including manual interventions, optimization, or fine-tuning. In this paper, we introduce a skeleton-aware latent diffusion (SALAD), a model that explicitly captures the intricate inter-relationships between joints, frames, and words. Furthermore, by leveraging cross-attention maps produced during the generation process, we enable the attention-based zero-shot text-driven motion editing using a pre-trained SALAD model, requiring no additional user input beyond text prompts. Our approach significantly outperforms previous methods in terms of text-motion alignment without compromising generation quality, and demonstrates practical versatility by providing diverse editing capabilities beyond generation. Code is available at project page.
|
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] |
167 |
Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network
|
[
"Haifeng Zhang",
"Qinghui He",
"Xiuli Bi",
"Weisheng Li",
"Bo Liu",
"Bin Xiao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Towards_Universal_AI-Generated_Image_Detection_by_Variational_Information_Bottleneck_Network_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Towards_Universal_AI-Generated_Image_Detection_by_Variational_Information_Bottleneck_Network_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Haifeng and He, Qinghui and Bi, Xiuli and Li, Weisheng and Liu, Bo and Xiao, Bin},
title = {Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23828-23837}
}
|
The rapid advancement of generative models has significantly improved the quality of generated images. Meanwhile, it challenges information authenticity and credibility. Current generated image detection methods based on large-scale pre-trained multimodal models have achieved impressive results. Although these models provide abundant features, the authentication task-related features are often submerged. Consequently, those authentication task-irrelated features cause models to learn superficial biases, thereby harming their generalization performance across different model genera (e.g., GANs and Diffusion Models). To this end, we proposed VIB-Net, which uses Variational Information Bottlenecks to enforce authentication task-related feature learning. We tested and analyzed the proposed method and existing methods on samples generated by 17 different generative models. Compared to SOTA methods, VIB-Net achieved a 5.55% improvement in mAP and a 9.33% increase in accuracy. Notably, in generalization tests on unseen generative models from different series, VIB-Net improved mAP by 12.48% and accuracy by 23.59% over SOTA methods. The code is available at https://github.com/oceanzhf/VIBAIGCDetect.
|
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] |
168 |
HSI: A Holistic Style Injector for Arbitrary Style Transfer
|
[
"Shuhao Zhang",
"Hui Kang",
"Yang Liu",
"Fang Mei",
"Hongjuan Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_HSI_A_Holistic_Style_Injector_for_Arbitrary_Style_Transfer_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_HSI_A_Holistic_Style_Injector_for_Arbitrary_Style_Transfer_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_HSI_A_Holistic_CVPR_2025_supplemental.pdf
|
2502.04369
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Shuhao and Kang, Hui and Liu, Yang and Mei, Fang and Li, Hongjuan},
title = {HSI: A Holistic Style Injector for Arbitrary Style Transfer},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23433-23442}
}
|
Attention-based arbitrary style transfer methods have gained significant attention recently due to their impressive ability to synthesize style details. However, the point-wise matching within the attention mechanism may overly focus on local patterns such that neglect the remarkable global features of style images. Additionally, when processing large images, the quadratic complexity of the attention mechanism will bring high computational load. To alleviate above problems, we propose Holistic Style Injector (HSI), a novel attention-style transformation module to deliver artistic expression of target style. Specifically, HSI performs stylization only based on global style representation that is more in line with the characteristics of style transfer, to avoid generating local disharmonious patterns in stylized images. Moreover, we propose a dual relation learning mechanism inside the HSI to dynamically render images by leveraging semantic similarity in content and style, ensuring the stylized images preserve the original content and improve style fidelity. Note that the proposed HSI achieves linear computational complexity because it establishes feature mapping through element-wise multiplication rather than matrix multiplication. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches in both effectiveness and efficiency.
|
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] |
169 |
LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping
|
[
"Pascal Chang",
"Sergio Sancho",
"Jingwei Tang",
"Markus Gross",
"Vinicius Azevedo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_LookingGlass_Generative_Anamorphoses_via_Laplacian_Pyramid_Warping_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_LookingGlass_Generative_Anamorphoses_via_Laplacian_Pyramid_Warping_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_LookingGlass_Generative_Anamorphoses_CVPR_2025_supplemental.zip
|
2504.08902
|
@InProceedings{Chang_2025_CVPR,
author = {Chang, Pascal and Sancho, Sergio and Tang, Jingwei and Gross, Markus and Azevedo, Vinicius},
title = {LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24-33}
}
|
Anamorphosis refers to a category of images that are intentionally distorted, making them unrecognizable when viewed directly. Their true form only reveals itself when seen from a specific viewpoint, which can be through some catadioptric device like a mirror or a lens. While the construction of these mathematical devices can be traced back to as early as the 17th century, they are only interpretable when viewed from a specific vantage point and tend to lose meaning when seen normally. In this paper, we revisit these famous optical illusions with a generative twist. With the help of latent rectified flow models, we propose a method to create anamorphic images that still retain a valid interpretation when viewed directly. To this end, we introduce Laplacian Pyramid Warping, a frequency-aware image warping technique key to generating high-quality visuals. Our work extends Visual Anagrams (Geng et al. 2024) to latent space models and to a wider range of spatial transforms, enabling the creation of novel generative perceptual illusions.
|
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] |
170 |
V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy
|
[
"Jiayin Zhao",
"Zhenqi Fu",
"Tao Yu",
"Hui Qiao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_V2V3D_View-to-View_Denoised_3D_Reconstruction_for_Light_Field_Microscopy_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_V2V3D_View-to-View_Denoised_3D_Reconstruction_for_Light_Field_Microscopy_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_V2V3D_View-to-View_Denoised_CVPR_2025_supplemental.pdf
|
2504.07853
|
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Jiayin and Fu, Zhenqi and Yu, Tao and Qiao, Hui},
title = {V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26451-26461}
}
|
Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. However, existing LFM reconstruction algorithms are highly sensitive to sensor noise or require hard-to-get ground-truth annotated data for training. To address these challenges, this paper introduces V2V3D, an unsupervised view2view-based framework that establishes a new paradigm for joint optimization of image denoising and 3D reconstruction in a unified architecture. We assume that the LF images are derived from a consistent 3D signal, with the noise in each view being independent. This enables V2V3D to incorporate the principle of noise2noise for effective denoising. To enhance the recovery of high-frequency details, we propose a novel wave-optics-based feature alignment technique, which transforms the point spread function, used for forward propagation in wave optics, into convolution kernels specifically designed for feature alignment. Moreover, we introduce an LFM dataset containing LF images and their corresponding 3D intensity volumes. Extensive experiments demonstrate that our approach achieves high computational efficiency and outperforms the other state-of-the-art methods. These advancements position V2V3D as a promising solution for 3D imaging under challenging conditions. Our code and dataset will be publicly accessible at https://joey1998hub.github.io/V2V3D/.
|
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] |
171 |
DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
|
[
"Erjian Guo",
"Zhen Zhao",
"Zicheng Wang",
"Tong Chen",
"Yunyi Liu",
"Luping Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_DiN_Diffusion_Model_for_Robust_Medical_VQA_with_Semantic_Noisy_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_DiN_Diffusion_Model_for_Robust_Medical_VQA_with_Semantic_Noisy_CVPR_2025_paper.pdf
| null |
2503.18536
|
@InProceedings{Guo_2025_CVPR,
author = {Guo, Erjian and Zhao, Zhen and Wang, Zicheng and Chen, Tong and Liu, Yunyi and Zhou, Luping},
title = {DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14337-14346}
}
|
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels and limited high-quality datasets remains underexplored. To address this, we establish the first benchmark for noisy labels in Med-VQA by simulating human mislabeling with semantically designed noise types. More importantly, we introduce the DiN framework, which leverages a diffusion model to handle noisy labels in Med-VQA. Unlike the dominant classification-based VQA approaches that directly predict answers, our Answer Diffuser (AD) module employs a coarse-to-fine process, refining answer candidates with a diffusion model for improved accuracy. The Answer Condition Generator (ACG) further enhances this process by generating task-specific conditional information via integrating answer embeddings with fused image-question features. To address label noise, our Noisy Label Refinement(NLR) module introduces a robust loss function and dynamic answer adjustment to further boost the performance of the AD module. Our DiN framework consistently outperforms existing methods across multiple benchmarks with varying noise levels.
|
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] |
172 |
Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images
|
[
"Zheng Chen",
"Chenming Wu",
"Zhelun Shen",
"Chen Zhao",
"Weicai Ye",
"Haocheng Feng",
"Errui Ding",
"Song-Hai Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Splatter-360_Generalizable_360_Gaussian_Splatting_for_Wide-baseline_Panoramic_Images_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Splatter-360_Generalizable_360_Gaussian_Splatting_for_Wide-baseline_Panoramic_Images_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Splatter-360_Generalizable_360_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Chen_2025_CVPR,
author = {Chen, Zheng and Wu, Chenming and Shen, Zhelun and Zhao, Chen and Ye, Weicai and Feng, Haocheng and Ding, Errui and Zhang, Song-Hai},
title = {Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21590-21599}
}
|
Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent distortions. Although existing 3D Gaussian splatting (3DGS) methods can produce photo-realistic views under narrow baselines, they often overfit the training views when dealing with wide-baseline panoramic images due to the difficulty in learning precise geometry from sparse 360^ \circ views. This paper presents Splatter-360, a novel end-to-end generalizable 3DGS framework designed to handle wide-baseline panoramic images. Unlike previous approaches, Splatter-360 performs multi-view matching directly in the spherical domain by constructing a spherical cost volume through a spherical sweep algorithm, enhancing the network's depth perception and geometry estimation. Additionally, we introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images and integrate cross-view attention to improve feature interactions across multiple viewpoints. This enables robust 3D-aware feature representations and real-time rendering capabilities. Experimental results on the HM3D and Replica demonstrate that Splatter-360 significantly outperforms state-of-the-art NeRF and 3DGS methods (e.g., PanoGRF, MVSplat, DepthSplat, and HiSplat) in both synthesis quality and generalization performance for wide-baseline panoramic images. Code and trained models are available at https://3d-aigc.github.io/Splatter-360/.
|
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] |
173 |
ShowMak3r: Compositional TV Show Reconstruction
|
[
"Sangmin Kim",
"Seunguk Do",
"Jaesik Park"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_ShowMak3r_Compositional_TV_Show_Reconstruction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ShowMak3r_Compositional_TV_Show_Reconstruction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_ShowMak3r_Compositional_TV_CVPR_2025_supplemental.pdf
|
2504.19584
|
@InProceedings{Kim_2025_CVPR,
author = {Kim, Sangmin and Do, Seunguk and Park, Jaesik},
title = {ShowMak3r: Compositional TV Show Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {864-874}
}
|
Reconstructing dynamic radiance fields from video clips is challenging, especially when entertainment videos like TV shows are given. Many challenges make the reconstruction difficult due to (1) actors occluding with each other and having diverse facial expressions, (2) cluttered stages, and (3) small baseline views or sudden shot changes. To address these issues, we present ShowMak3r, a comprehensive reconstruction pipeline that allows the editing of scenes like how video clips are made in a production control room. In ShowMak3r, a 3DLocator module locates recovered actors on the stage using depth prior, and estimates unseen human poses via interpolation. The proposed ShotMatcher module then tracks the actors under shot changes. Furthermore, ShowMak3r introduces a face-fitting network that dynamically recovers the actors' expressions. Experiments on Sitcoms3D dataset show that our pipeline can reassemble TV show scenes with new cameras at different timestamps. We also demonstrate that ShowMak3r enables interesting applications such as synthetic shot-making, actor relocation, insertion, deletion, and pose manipulation. Project page : https://nstar1125.github.io/showmak3r
|
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] |
174 |
CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging
|
[
"Zhiwei Ling",
"Yachen Chang",
"Hailiang Zhao",
"Xinkui Zhao",
"Kingsum Chow",
"Shuiguang Deng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ling_CADRef_Robust_Out-of-Distribution_Detection_via_Class-Aware_Decoupled_Relative_Feature_Leveraging_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ling_CADRef_Robust_Out-of-Distribution_Detection_via_Class-Aware_Decoupled_Relative_Feature_Leveraging_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ling_CADRef_Robust_Out-of-Distribution_CVPR_2025_supplemental.pdf
|
2503.00325
|
@InProceedings{Ling_2025_CVPR,
author = {Ling, Zhiwei and Chang, Yachen and Zhao, Hailiang and Zhao, Xinkui and Chow, Kingsum and Deng, Shuiguang},
title = {CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4968-4977}
}
|
Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective OOD detection to ensure the safe deployment of DNNs in real-world settings. Existing post-hoc OOD detection methods primarily enhance the discriminative power of logit-based approaches by reshaping sample features, yet they often neglect critical information inherent in the features themselves. In this paper, we propose the \underline C lass-\underline A ware \underline Re lative \underline F eature-based method (CARef), which utilizes the error between a sample's feature and its class-aware average feature as a discriminative criterion. To further refine this approach, we introduce the \underline C lass-\underline A ware \underline D ecoupled \underline Re lative \underline F eature-based method (CADRef), which decouples sample features based on the alignment of signs between the relative feature and corresponding model weights, enhancing the discriminative capabilities of CARef.Extensive experimental results across multiple datasets and models demonstrate that both proposed methods exhibit effectiveness and robustness in OOD detection compared to state-of-the-art methods. Specifically, our two methods outperform the best baseline by 2.82% and 3.27% in AUROC, with improvements of 4.03% and 6.32% in FPR95, respectively.
|
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] |
175 |
S^3-Face: SSS-Compliant Facial Reflectance Estimation via Diffusion Priors
|
[
"Xingyu Ren",
"Jiankang Deng",
"Yuhao Cheng",
"Wenhan Zhu",
"Yichao Yan",
"Xiaokang Yang",
"Stefanos Zafeiriou",
"Chao Ma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_S3-Face_SSS-Compliant_Facial_Reflectance_Estimation_via_Diffusion_Priors_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_S3-Face_SSS-Compliant_Facial_Reflectance_Estimation_via_Diffusion_Priors_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_S3-Face_SSS-Compliant_Facial_CVPR_2025_supplemental.zip
| null |
@InProceedings{Ren_2025_CVPR,
author = {Ren, Xingyu and Deng, Jiankang and Cheng, Yuhao and Zhu, Wenhan and Yan, Yichao and Yang, Xiaokang and Zafeiriou, Stefanos and Ma, Chao},
title = {S{\textasciicircum}3-Face: SSS-Compliant Facial Reflectance Estimation via Diffusion Priors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16051-16060}
}
|
Recent 3D face reconstruction methods have made remarkable advancements, yet achieving high-quality facial reflectance from monocular input remains challenging. Existing methods rely on the light-stage captured data to learn facial reflectance models. However, limited subject diversity in these datasets poses challenges in achieving good generalization and broad applicability. This motivates us to explore whether the extensive priors captured in recent generative diffusion models (e.g., Stable Diffusion) can enable more generalizable facial reflectance estimation as these models have been pre-trained on large-scale internet image collections containing rich visual patterns. In this paper, we introduce the use of Stable Diffusion as a prior for facial reflectance estimation, achieving robust results with minimal captured data for fine-tuning. We present S^3-Face, a comprehensive framework capable of producing SSS-compliant skin reflectance from in-the-wild images. Our method adopts a two-stage training approach: in the first stage, DSN-Net is trained to predict diffuse albedo, specular albedo, and normal maps from in-the-wild images using a novel joint reflectance attention module. In the second stage, HM-Net is trained to generate hemoglobin and melanin maps based on the diffuse albedo predicted in the first stage, yielding SSS-compliant and detailed reflectance maps. Extensive experiments demonstrate that our method achieves strong generalization and produces high-fidelity, SSS-compliant facial reflectance reconstructions.
|
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] |
176 |
FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding
|
[
"Rong Gao",
"Xin Liu",
"Zhuozhao Hu",
"Bohao Xing",
"Baiqiang Xia",
"Zitong Yu",
"Heikki Kälviäinen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_FSBench_A_Figure_Skating_Benchmark_for_Advancing_Artistic_Sports_Understanding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_FSBench_A_Figure_Skating_Benchmark_for_Advancing_Artistic_Sports_Understanding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_FSBench_A_Figure_CVPR_2025_supplemental.pdf
|
2504.19514
|
@InProceedings{Gao_2025_CVPR,
author = {Gao, Rong and Liu, Xin and Hu, Zhuozhao and Xing, Bohao and Xia, Baiqiang and Yu, Zitong and K\"alvi\"ainen, Heikki},
title = {FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13595-13605}
}
|
Figure skating, known as the "Art on Ice," is among the most artistic sports, challenging to understand due to its blend of technical elements (like jumps and spins) and overall artistic expression. Existing figure skating datasets mainly focus on single tasks, such as action recognition or scoring, lacking comprehensive annotations for both technical and artistic evaluation. Current sports research is largely centered on ball games, with limited relevance to artistic sports like figure skating. To address this, we introduce FSAnno, a large-scale dataset advancing artistic sports understanding through figure skating. FSAnno includes an open-access training and test dataset, alongside a benchmark dataset, FSBench, for fair model evaluation. FSBench consists of FSBench-Text, with multiple-choice questions and explanations, and FSBench-Motion, containing multimodal data and Question and Answer (QA) pairs, supporting tasks from technical analysis to performance commentary. Initial tests on FSBench reveal significant limitations in existing models' understanding of artistic sports. We hope FSBench will become a key tool for evaluating and enhancing model comprehension of figure skating. All data, models, and more details are available at: https://github.com/Moomin-Fin/Ano.
|
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] |
177 |
Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation
|
[
"Jiaxin Cai",
"Jingze Su",
"Qi Li",
"Wenjie Yang",
"Shu Wang",
"Tiesong Zhao",
"Shengfeng He",
"Wenxi Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_Keep_the_Balance_A_Parameter-Efficient_Symmetrical_Framework_for_RGBX_Semantic_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Keep_the_Balance_A_Parameter-Efficient_Symmetrical_Framework_for_RGBX_Semantic_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Keep_the_Balance_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Cai_2025_CVPR,
author = {Cai, Jiaxin and Su, Jingze and Li, Qi and Yang, Wenjie and Wang, Shu and Zhao, Tiesong and He, Shengfeng and Liu, Wenxi},
title = {Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10587-10598}
}
|
Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations of attention mechanism for modality fusion, which inadvertently introduces noise across modalities. To mitigate this noise, we propose a dynamic sparse cross-modality fusion module to facilitate effective and efficient cross-modality fusion. To further strengthen the above two modules, we propose a training strategy that leverages accurately predicted dual-modality results to self-teach the single-modality outcomes. In comprehensive experiments, we demonstrate that our method outperforms previous state-of-the-art approaches across six multimodal segmentation scenarios with minimal computation cost.
|
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] |
178 |
VideoDirector: Precise Video Editing via Text-to-Video Models
|
[
"Yukun Wang",
"Longguang Wang",
"Zhiyuan Ma",
"Qibin Hu",
"Kai Xu",
"Yulan Guo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VideoDirector_Precise_Video_Editing_via_Text-to-Video_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VideoDirector_Precise_Video_Editing_via_Text-to-Video_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VideoDirector_Precise_Video_CVPR_2025_supplemental.zip
|
2411.17592
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Yukun and Wang, Longguang and Ma, Zhiyuan and Hu, Qibin and Xu, Kai and Guo, Yulan},
title = {VideoDirector: Precise Video Editing via Text-to-Video Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2589-2598}
}
|
Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content distortion. Consequently, current video editing methods primarily rely on T2I models, which inherently lack temporal-coherence generative ability, often resulting in inferior editing results. In this paper, we attribute the failure of the typical editing paradigm to: 1) Tightly Spatial-temporal Coupling. The vanilla pivotal-based inversion strategy struggles to disentangle spatial-temporal information in the video diffusion model; 2) Complicated Spatial-temporal Layout. The vanilla cross-attention control strategy is deficient in preserving the unedited content. To address these limitations, we propose a spatial-temporal decoupled guidance (STDG) and multi-frame null-text optimization strategy to provide pivotal temporal cues for more precise pivotal inversion. Furthermore, we introduce a self-attention control strategy to maintain higher fidelity for precise partial content editing. Experimental results demonstrate that our method (termed VideoDirector) effectively harnesses the powerful temporal generation capabilities of T2V models, producing edited videos with state-of-the-art performance in accuracy, motion smoothness, realism, and fidelity to unedited content.
|
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] |
179 |
LLM-driven Multimodal and Multi-Identity Listening Head Generation
|
[
"Peiwen Lai",
"Weizhi Zhong",
"Yipeng Qin",
"Xiaohang Ren",
"Baoyuan Wang",
"Guanbin Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_LLM-driven_Multimodal_and_Multi-Identity_Listening_Head_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_LLM-driven_Multimodal_and_Multi-Identity_Listening_Head_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_LLM-driven_Multimodal_and_CVPR_2025_supplemental.zip
| null |
@InProceedings{Lai_2025_CVPR,
author = {Lai, Peiwen and Zhong, Weizhi and Qin, Yipeng and Ren, Xiaohang and Wang, Baoyuan and Li, Guanbin},
title = {LLM-driven Multimodal and Multi-Identity Listening Head Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10656-10666}
}
|
Generating natural listener responses in conversational scenarios is crucial for creating engaging digital humans and avatars. Recent work has shown that large language models (LLMs) can be effectively leveraged for this task, demonstrating remarkable capabilities in generating contextually appropriate listener behaviors. However, current LLM-based methods face two critical limitations: they rely solely on speech content, overlooking other crucial communication signals, and they entangle listener identity with response generation, compromising output fidelity and generalization. In this work, we present a novel framework that addresses these limitations while maintaining the advantages of LLMs. Our approach introduces a Multimodal-LM architecture that jointly processes speech content, acoustics, and speaker emotion, capturing the full spectrum of communication cues. Additionally, we propose an identity disentanglement strategy using instance normalization and adaptive instance normalization in a VQ-VAE framework, enabling high-fidelity listening head synthesis with flexible identity control. Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of response naturalness and fidelity, while enabling effective identity control without retraining.
|
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] |
180 |
Towards Understanding How Knowledge Evolves in Large Vision-Language Models
|
[
"Sudong Wang",
"Yunjian Zhang",
"Yao Zhu",
"Jianing Li",
"Zizhe Wang",
"Yanwei Liu",
"Xiangyang Ji"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Towards_Understanding_How_Knowledge_Evolves_in_Large_Vision-Language_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Towards_Understanding_How_Knowledge_Evolves_in_Large_Vision-Language_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Towards_Understanding_How_CVPR_2025_supplemental.pdf
|
2504.02862
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Sudong and Zhang, Yunjian and Zhu, Yao and Li, Jianing and Wang, Zizhe and Liu, Yanwei and Ji, Xiangyang},
title = {Towards Understanding How Knowledge Evolves in Large Vision-Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29858-29868}
}
|
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications. However, understanding their internal working mechanisms has continued to puzzle researchers, which in turn limits the further enhancement of their capabilities. In this paper, we seek to investigate how multimodal knowledge evolves and eventually induces natural languages in LVLMs. We design a series of novel strategies for analyzing internal knowledge within LVLMs, and delve into the evolution of multimodal knowledge from three levels, including single token probabilities, token probability distributions, and feature encodings. In this process, we identify two key nodes in knowledge evolution: the critical layers and the mutation layers, dividing the evolution process into three stages: rapid evolution, stabilization, and mutation. Our research is the first to reveal the trajectory of knowledge evolution in LVLMs, providing a fresh perspective for understanding their underlying mechanisms.
|
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] |
181 |
A Unified, Resilient, and Explainable Adversarial Patch Detector
|
[
"Vishesh Kumar",
"Akshay Agarwal"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_A_Unified_Resilient_and_Explainable_Adversarial_Patch_Detector_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumar_A_Unified_Resilient_and_Explainable_Adversarial_Patch_Detector_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumar_A_Unified_Resilient_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Kumar_2025_CVPR,
author = {Kumar, Vishesh and Agarwal, Akshay},
title = {A Unified, Resilient, and Explainable Adversarial Patch Detector},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30387-30397}
}
|
Deep Neural Networks (DNNs), backbone architecture in `almost' every computer vision task, are vulnerable to adversarial attacks, particularly physical out-of-distribution (OOD) adversarial patches. Existing defense models often struggle with interpreting these attacks in ways that align with human visual perception. Our proposed AdvPatchXAI approach introduces a generalized, robust, and explainable defense algorithm designed to defend DNNs against physical adversarial threats. AdvPatchXAI employs a novel patch decorrelation loss that reduces feature redundancy and enhances the distinctiveness of patch representations, enabling better generalization across unseen adversarial scenarios. It learns prototypical parts self-supervised, enhancing interpretability and correlation with human vision. The model utilizes a sparse linear layer for classification, making the decision process globally interpretable through a set of learned prototypes and locally explainable by pinpointing relevant prototypes within an image. Our comprehensive evaluation shows that AdvPatchXAI closes the "semantic" gap between latent space and pixel space and effectively handles unseen adversarial patches even perturbed with unseen corruptions, thereby significantly advancing DNN robustness in practical settings(https://github.com/tbvl22/Unified-resilient-and-Explainable-Adversarial-Patch-detector).
|
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] |
182 |
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation
|
[
"Weiming Ren",
"Huan Yang",
"Jie Min",
"Cong Wei",
"Wenhu Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_VISTA_Enhancing_Long-Duration_and_High-Resolution_Video_Understanding_by_Video_Spatiotemporal_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_VISTA_Enhancing_Long-Duration_and_High-Resolution_Video_Understanding_by_Video_Spatiotemporal_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_VISTA_Enhancing_Long-Duration_CVPR_2025_supplemental.pdf
|
2412.00927
|
@InProceedings{Ren_2025_CVPR,
author = {Ren, Weiming and Yang, Huan and Min, Jie and Wei, Cong and Chen, Wenhu},
title = {VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3804-3814}
}
|
Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-quality datasets. To address this issue from a data-centric perspective, we propose VISTA, a simple yet effective video spatiotemporal augmentation framework that synthesizes long-duration and high-resolution video instruction-following pairs from existing video-caption datasets. VISTA spatially and temporally combines videos to create new synthetic videos with extended durations and enhanced resolutions, and subsequently produces question-answer pairs pertaining to these newly synthesized videos. Based on this paradigm, we develop seven video augmentation methods and curate VISTA-400K, a video instruction-following dataset aimed at enhancing long-duration and high-resolution video understanding. Finetuning various video LMMs on our data resulted in an average improvement of 3.3% across four challenging benchmarks for long-video understanding. Furthermore, we introduce the first comprehensive high-resolution video understanding benchmark HRVideoBench, on which our finetuned models achieve a 6.5% performance gain. These results highlight the effectiveness of our framework.
|
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] |
183 |
Structured 3D Latents for Scalable and Versatile 3D Generation
|
[
"Jianfeng Xiang",
"Zelong Lv",
"Sicheng Xu",
"Yu Deng",
"Ruicheng Wang",
"Bowen Zhang",
"Dong Chen",
"Xin Tong",
"Jiaolong Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_Structured_3D_Latents_for_Scalable_and_Versatile_3D_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_Structured_3D_Latents_for_Scalable_and_Versatile_3D_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_Structured_3D_Latents_CVPR_2025_supplemental.pdf
|
2412.01506
|
@InProceedings{Xiang_2025_CVPR,
author = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
title = {Structured 3D Latents for Scalable and Versatile 3D Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21469-21480}
}
|
We introduce a novel 3D generation method for versatile and high-quality 3D asset creation.The cornerstone is a unified Structured LATent (SLAT) representation which allows decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features extracted from a powerful vision foundation model, comprehensively capturing both structural (geometry) and textural (appearance) information while maintaining flexibility during decoding.We employ rectified flow transformers tailored for SLAT as our 3D generation models and train models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. Our model generates high-quality results with text or image conditions, significantly surpassing existing methods, including recent ones at similar scales. We showcase flexible output format selection and local 3D editing capabilities which were not offered by previous models. Code, model, and data will be released.
|
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] |
184 |
GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding
|
[
"Yuki Kawana",
"Shintaro Shiba",
"Quan Kong",
"Norimasa Kobori"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kawana_GA3CE_Unconstrained_3D_Gaze_Estimation_with_Gaze-Aware_3D_Context_Encoding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kawana_GA3CE_Unconstrained_3D_Gaze_Estimation_with_Gaze-Aware_3D_Context_Encoding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kawana_GA3CE_Unconstrained_3D_CVPR_2025_supplemental.pdf
|
2505.10671
|
@InProceedings{Kawana_2025_CVPR,
author = {Kawana, Yuki and Shiba, Shintaro and Kong, Quan and Kobori, Norimasa},
title = {GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3081-3090}
}
|
We propose a novel 3D gaze estimation approach that learns spatial relationships between the subject and objects in the scene, and outputs 3D gaze direction. Our method targets unconstrained settings, including cases where close-up views of the subject's eyes are unavailable, such as when the subject is distant or facing away. Previous approaches typically rely on either 2D appearance alone or incorporate limited spatial cues using depth maps in the non-learnable post-processing step. Estimating 3D gaze direction from 2D observations in these scenarios is challenging; variations in subject pose, scene layout, and gaze direction, combined with differing camera poses, yield diverse 2D appearances and 3D gaze directions even when targeting the same 3D scene. To address this issue, we propose GA3CE: Gaze-Aware 3D Context Encoding. Our method represents subject and scene using 3D poses and object positions, treating them as 3D context to learn spatial relationships in 3D space. Inspired by human vision, we align this context in an egocentric space, significantly reducing spatial complexity. Furthermore, we propose D^3 (direction-distance-decomposed) positional encoding to better capture the spatial relationship between 3D context and gaze direction in direction and distance space. Experiments demonstrate substantial improvements, reducing mean angle error by 13%-37% compared to leading baselines on benchmark datasets in single-frame settings.
|
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] |
185 |
Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects
|
[
"Weimin Qiu",
"Jieke Wang",
"Meng Tang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Self-Cross_Diffusion_Guidance_for_Text-to-Image_Synthesis_of_Similar_Subjects_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Self-Cross_Diffusion_Guidance_for_Text-to-Image_Synthesis_of_Similar_Subjects_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Self-Cross_Diffusion_Guidance_CVPR_2025_supplemental.pdf
|
2411.18936
|
@InProceedings{Qiu_2025_CVPR,
author = {Qiu, Weimin and Wang, Jieke and Tang, Meng},
title = {Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23528-23538}
}
|
Diffusion models achieved unprecedented fidelity and diversity for synthesizing image, video, 3D assets, etc. However, subject mixing is an unresolved issue for diffusion-based image synthesis, particularly for synthesizing multiple similar-looking subjects. We propose Self-Cross Diffusion Guidance to penalize the overlap between cross-attention maps and the aggregated self-attention map. Compared to previous methods based on self-attention or cross-attention alone, our guidance is more effective in eliminating subject mixing. What's more, our guidance addresses subject mixing for all relevant patches beyond the most discriminant one, e.g., the beak of a bird. For each subject, we aggregate self-attention maps of patches with higher cross-attention values. Thus, the aggregated self-attention map forms a region that the whole subject attends to. Our training-free method boosts the performance of both Unet-based and Transformer-based diffusion models such as the Stable Diffusion series. We also release a similar subjects dataset (SSD), a challenging benchmark, and utilize GPT-4o for automatic and reliable evaluation. Extensive qualitative and quantitative results demonstrate the effectiveness of our self-cross diffusion guidance.
|
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] |
186 |
RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos
|
[
"Yuxin Yao",
"Zhi Deng",
"Junhui Hou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_RigGS_Rigging_of_3D_Gaussians_for_Modeling_Articulated_Objects_in_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_RigGS_Rigging_of_3D_Gaussians_for_Modeling_Articulated_Objects_in_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_RigGS_Rigging_of_CVPR_2025_supplemental.pdf
|
2503.16822
|
@InProceedings{Yao_2025_CVPR,
author = {Yao, Yuxin and Deng, Zhi and Hou, Junhui},
title = {RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5592-5601}
}
|
This paper considers the problem of modeling articulated objects captured in 2D videos to enable novel view synthesis, while also being easily editable, drivable, and reposable. To tackle this challenging problem, we propose RigGS, a new paradigm that leverages 3D Gaussian representation and skeleton-based motion representation to model dynamic objects without utilizing additional template priors. Specifically, we first propose skeleton-aware node-controlled deformation, which deforms a canonical 3D Gaussian representation over time to initialize the modeling process, producing candidate skeleton nodes that are further simplified into a sparse 3D skeleton according to their motion and semantic information. Subsequently, based on the resulting skeleton, we design learnable skin deformations and pose-dependent detailed deformations, thereby easily deforming the 3D Gaussian representation to generate new actions and render further high-quality images from novel views. Extensive experiments demonstrate that our method can generate realistic new actions easily for objects and achieve high-quality rendering.
|
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] |
187 |
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising
|
[
"Feiran Li",
"Haiyang Jiang",
"Daisuke Iso"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Noise_Modeling_in_One_Hour_Minimizing_Preparation_Efforts_for_Self-supervised_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Noise_Modeling_in_One_Hour_Minimizing_Preparation_Efforts_for_Self-supervised_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Noise_Modeling_in_CVPR_2025_supplemental.pdf
|
2505.00045
|
@InProceedings{Li_2025_CVPR,
author = {Li, Feiran and Jiang, Haiyang and Iso, Daisuke},
title = {Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5699-5708}
}
|
Noise synthesis is a promising solution for addressing the data shortage problem in data-driven low-light RAW image denoising. However, accurate noise synthesis methods often necessitate labor-intensive calibration and profiling procedures during preparation, preventing them from landing to practice at scale. This work introduces a practically simple noise synthesis pipeline based on detailed analyses of noise properties and extensive justification of widespread techniques. Compared to other approaches, our proposed pipeline eliminates the cumbersome system gain calibration and signal-independent noise profiling steps, reducing the preparation time for noise synthesis from days to hours. Meanwhile, our method exhibits strong denoising performance, showing an up to 0.54dB PSNR improvement over the current state-of-the-art noise synthesis technique.
|
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] |
188 |
Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks
|
[
"Junying Wang",
"Hongyuan Zhang",
"Yuan Yuan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Adv-CPG_A_Customized_Portrait_Generation_Framework_with_Facial_Adversarial_Attacks_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Adv-CPG_A_Customized_Portrait_Generation_Framework_with_Facial_Adversarial_Attacks_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Adv-CPG_A_Customized_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Junying and Zhang, Hongyuan and Yuan, Yuan},
title = {Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21001-21010}
}
|
Recent Customized Portrait Generation (CPG) methods, taking a facial image and a textual prompt as inputs, have attracted substantial attention. Although these methods generate high-fidelity portraits, they fail to prevent the generated portraits from being tracked and misused by malicious face recognition systems. To address this, this paper proposes a Customized Portrait Generation framework with facial Adversarial attacks (Adv-CPG). Specifically, to achieve facial privacy protection, we devise a lightweight local ID encryptor and an encryption enhancer. They implement progressive double-layer encryption protection by directly injecting the target identity and adding additional identity guidance, respectively. Furthermore, to accomplish fine-grained and personalized portrait generation, we develop a multi-modal image customizer capable of generating controlled fine-grained facial features. To the best of our knowledge, Adv-CPG is the first study that introduces facial adversarial attacks into CPG. Extensive experiments demonstrate the superiority of Adv-CPG, e.g., the average attack success rate of the proposed Adv-CPG is 28.1% and 2.86% higher compared to the SOTA noise-based attack methods and unconstrained attack methods, respectively.
|
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] |
189 |
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
|
[
"Kazi Sajeed Mehrab",
"M. Maruf",
"Arka Daw",
"Abhilash Neog",
"Harish Babu Manogaran",
"Mridul Khurana",
"Zhenyang Feng",
"Bahadir Altintas",
"Yasin Bakis",
"Elizabeth G Campolongo",
"Matthew J Thompson",
"Xiaojun Wang",
"Hilmar Lapp",
"Tanya Berger-Wolf",
"Paula Mabee",
"Henry Bart",
"Wei-Lun Chao",
"Wasila M Dahdul",
"Anuj Karpatne"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Mehrab_Fish-Vista_A_Multi-Purpose_Dataset_for_Understanding__Identification_of_Traits_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Mehrab_Fish-Vista_A_Multi-Purpose_Dataset_for_Understanding__Identification_of_Traits_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mehrab_Fish-Vista_A_Multi-Purpose_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Mehrab_2025_CVPR,
author = {Mehrab, Kazi Sajeed and Maruf, M. and Daw, Arka and Neog, Abhilash and Manogaran, Harish Babu and Khurana, Mridul and Feng, Zhenyang and Altintas, Bahadir and Bakis, Yasin and Campolongo, Elizabeth G and Thompson, Matthew J and Wang, Xiaojun and Lapp, Hilmar and Berger-Wolf, Tanya and Mabee, Paula and Bart, Henry and Chao, Wei-Lun and Dahdul, Wasila M and Karpatne, Anuj},
title = {Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24275-24285}
}
|
We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using machine learning and computer vision methods. Fish-Vista contains 69,269 annotated images spanning 4,316 fish species, curated and organized to serve three downstream tasks: species classification, trait identification, and trait segmentation. Our work makes two key contributions. First, we provide a fully reproducible data processing pipeline to process fish images sourced from various museum collections, contributing to the advancement of AI in biodiversity science. We annotate the images with carefully curated labels from biological databases and manual annotations to create an AI-ready dataset of visual traits. Second, our work offers fertile grounds for researchers to develop novel methods for a variety of problems in computer vision such as handling long-tailed distributions, out-of-distribution generalization, learning with weak labels, explainable AI, and segmenting small objects. Dataset and code for Fish-Vista are available at https://github.com/Imageomics/Fish-Vista
|
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] |
190 |
High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm
|
[
"Zhaoyi Tian",
"Feifeng Wang",
"Shiwei Wang",
"Zihao Zhou",
"Yao Zhu",
"Liquan Shen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_High_Dynamic_Range_Video_Compression_A_Large-Scale_Benchmark_Dataset_and_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_High_Dynamic_Range_Video_Compression_A_Large-Scale_Benchmark_Dataset_and_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_High_Dynamic_Range_CVPR_2025_supplemental.pdf
|
2503.00410
|
@InProceedings{Tian_2025_CVPR,
author = {Tian, Zhaoyi and Wang, Feifeng and Wang, Shiwei and Zhou, Zihao and Zhu, Yao and Shen, Liquan},
title = {High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7320-7330}
}
|
Recently, learned video compression (LVC) is undergoing a period of rapid development. However, due to absence of large and high-quality high dynamic range (HDR) video training data, LVC on HDR video is still unexplored. In this paper, we are the first to collect a large-scale HDR video benchmark dataset, named HDRVD2K, featuring huge quantity, diverse scenes and multiple motion types. HDRVD2K fills gaps of video training data and facilitate the development of LVC on HDR videos. Based on HDRVD2K, we further propose the first learned bit-depth scalable video compression (LBSVC) network for HDR videos by effectively exploiting bit-depth redundancy between videos of multiple dynamic ranges. To achieve this, we first propose a compression-friendly bit-depth enhancement module (BEM) to effectively predict original HDR videos based on compressed tone-mapped low dynamic range (LDR) videos and dynamic range prior, instead of reducing redundancy only through spatio-temporal predictions. Our method greatly improves the reconstruction quality and compression performance on HDR videos. Extensive experiments demonstrate the effectiveness of HDRVD2K on learned HDR video compression and great compression performance of our proposed LBSVC network.
|
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] |
191 |
OffsetOPT: Explicit Surface Reconstruction without Normals
|
[
"Huan Lei"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_OffsetOPT_Explicit_Surface_Reconstruction_without_Normals_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_OffsetOPT_Explicit_Surface_Reconstruction_without_Normals_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_OffsetOPT_Explicit_Surface_CVPR_2025_supplemental.pdf
|
2503.15763
|
@InProceedings{Lei_2025_CVPR,
author = {Lei, Huan},
title = {OffsetOPT: Explicit Surface Reconstruction without Normals},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11729-11738}
}
|
Neural surface reconstruction has been dominated by implicit representations with marching cubes for explicit surface extraction. However, those methods typically require high-quality normals for accurate reconstruction. We propose OffsetOPT, a method that reconstructs explicit surfaces directly from 3D point clouds and eliminates the need for point normals. The approach comprises two stages: first, we train a neural network to predict surface triangles based on local point geometry, given uniformly distributed training point clouds. Next, we apply the frozen network to reconstruct surfaces from unseen point clouds by optimizing a per-point offset to maximize the accuracy of triangle predictions. Compared to state-of-the-art methods, OffsetOPT not only excels at reconstructing overall surfaces but also significantly preserves sharp surface features. We demonstrate its accuracy on popular benchmarks, including small-scale shapes and large-scale open surfaces.
|
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] |
192 |
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models
|
[
"Junhyuk So",
"Jiwoong Shin",
"Chaeyeon Jang",
"Eunhyeok Park"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/So_PCM__Picard_Consistency_Model_for_Fast_Parallel_Sampling_of_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/So_PCM__Picard_Consistency_Model_for_Fast_Parallel_Sampling_of_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/So_PCM__Picard_CVPR_2025_supplemental.pdf
|
2503.19731
|
@InProceedings{So_2025_CVPR,
author = {So, Junhyuk and Shin, Jiwoong and Jang, Chaeyeon and Park, Eunhyeok},
title = {PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23313-23322}
}
|
Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output. Nonetheless, Picard iteration does not guarantee faster convergence, which can still result in slow generation in practice. In this work, we propose a new parallelization scheme, the Picard Consistency Model (PCM), which significantly reduces the number of generation steps in Picard iteration. Inspired by the consistency model, PCM is directly trained to predict the fixed-point solution, or the final output, at any stage of the convergence trajectory. Additionally, we introduce a new concept called model switching, which addresses PCM's limitations and ensures exact convergence. Extensive experiments demonstrate that PCM achieves up to a 2.71x speedup over sequential sampling and a 1.77x speedup over Picard iteration across various tasks, including image generation and robotic control.
|
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] |
193 |
CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesis
|
[
"Youngkyoon Jang",
"Eduardo Pérez-Pellitero"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_CoMapGS_Covisibility_Map-based_Gaussian_Splatting_for_Sparse_Novel_View_Synthesis_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_CoMapGS_Covisibility_Map-based_Gaussian_Splatting_for_Sparse_Novel_View_Synthesis_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_CoMapGS_Covisibility_Map-based_CVPR_2025_supplemental.zip
| null |
@InProceedings{Jang_2025_CVPR,
author = {Jang, Youngkyoon and P\'erez-Pellitero, Eduardo},
title = {CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26779-26788}
}
|
We propose Covisibility Map-based Gaussian Splatting (CoMapGS), designed to recover underrepresented sparse regions in sparse novel view synthesis. CoMapGS addresses both high- and low-uncertainty regions by constructing covisibility maps, enhancing initial point clouds, and applying uncertainty-aware weighted supervision using a proximity classifier. Our contributions are threefold: (1) CoMapGS reframes novel view synthesis by leveraging covisibility maps as a core component to address region-specific uncertainty; (2) Enhanced initial point clouds for both low- and high-uncertainty regions compensate for sparse COLMAP-derived point clouds, improving reconstruction quality and benefiting few-shot 3DGS methods; (3) Adaptive supervision with covisibility-score-based weighting and proximity classification achieves consistent performance gains across scenes with varying sparsity scores derived from covisibility maps. Experimental results demonstrate that CoMapGS outperforms state-of-the-art methods on datasets including Mip-NeRF 360 and LLFF.
|
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] |
194 |
Any-Resolution AI-Generated Image Detection by Spectral Learning
|
[
"Dimitrios Karageorgiou",
"Symeon Papadopoulos",
"Ioannis Kompatsiaris",
"Efstratios Gavves"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Karageorgiou_Any-Resolution_AI-Generated_Image_Detection_by_Spectral_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Karageorgiou_Any-Resolution_AI-Generated_Image_Detection_by_Spectral_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Karageorgiou_Any-Resolution_AI-Generated_Image_CVPR_2025_supplemental.pdf
|
2411.19417
|
@InProceedings{Karageorgiou_2025_CVPR,
author = {Karageorgiou, Dimitrios and Papadopoulos, Symeon and Kompatsiaris, Ioannis and Gavves, Efstratios},
title = {Any-Resolution AI-Generated Image Detection by Spectral Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18706-18717}
}
|
Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we build upon the key idea that the spectral distribution of real images constitutes both an invariant and highly discriminative pattern for AI-generated image detection. To model this under a self-supervised setup, we employ masked spectral learning using the pretext task of frequency reconstruction. Since generated images constitute out-of-distribution samples for this model, we propose spectral reconstruction similarity to capture this divergence. Moreover, we introduce spectral context attention, which enables our approach to efficiently capture subtle spectral inconsistencies in images of any resolution. Our spectral AI-generated image detection approach (SPAI) achieves a 5.5% absolute improvement in AUC over the previous state-of-the-art across 13 recent generative approaches, while exhibiting robustness against common online perturbations. Code is available on https://mever-team.github.io/spai.
|
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] |
195 |
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models
|
[
"Saeed Ranjbar Alvar",
"Gursimran Singh",
"Mohammad Akbari",
"Yong Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Alvar_DivPrune_Diversity-based_Visual_Token_Pruning_for_Large_Multimodal_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Alvar_DivPrune_Diversity-based_Visual_Token_Pruning_for_Large_Multimodal_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Alvar_DivPrune_Diversity-based_Visual_CVPR_2025_supplemental.pdf
|
2503.02175
|
@InProceedings{Alvar_2025_CVPR,
author = {Alvar, Saeed Ranjbar and Singh, Gursimran and Akbari, Mohammad and Zhang, Yong},
title = {DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9392-9401}
}
|
Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos. LMMs encode both text and visual data into tokens that are then combined and processed by an integrated Large Language Model (LLM). Including visual tokens substantially increases the total token count, often by thousands. The increased input length for LLM significantly raises the complexity of inference, resulting in high latency in LMMs. To address this issue, token pruning methods, which remove part of the visual tokens, are proposed. The existing token pruning methods either require extensive calibration and fine-tuning or rely on suboptimal importance metrics which results in increased redundancy among the retained tokens. In this paper, we first formulate token pruning as Max-Min Diversity Problem (MMDP) where the goal is to select a subset such that the diversity among the selected tokens is maximized. Then, we solve the MMDP to obtain the selected subset and prune the rest. The proposed method, DivPrune, reduces redundancy and achieves the highest diversity of the selected tokens. By ensuring high diversity, the selected tokens better represent the original tokens, enabling effective performance even at high pruning ratios without requiring fine-tuning. Extensive experiments with various LMMs show that DivPrune achieves state-of-the-art accuracy over 16 image- and video-language datasets. Additionally, DivPrune reduces both the end-to-end latency and GPU memory usage for the tested models. The code is available here.
|
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] |
196 |
Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?
|
[
"Yuechen Xie",
"Jie Song",
"Huiqiong Wang",
"Mingli Song"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Training_Data_Provenance_Verification_Did_Your_Model_Use_Synthetic_Data_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Training_Data_Provenance_Verification_Did_Your_Model_Use_Synthetic_Data_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Training_Data_Provenance_CVPR_2025_supplemental.zip
|
2503.09122
|
@InProceedings{Xie_2025_CVPR,
author = {Xie, Yuechen and Song, Jie and Wang, Huiqiong and Song, Mingli},
title = {Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23817-23827}
}
|
High-quality open-source text-to-image models have lowered the threshold for obtaining photorealistic images significantly, but also face potential risks of misuse. Specifically, suspects may use synthetic data generated by these generative models to train models for specific tasks without permission, when lacking real data resources especially. Protecting these generative models is crucial for the well-being of their owners. In this work, we propose the first method to this important yet unresolved issue, called Training data Provenance Verification (TrainProVe). The rationale behind TrainProVe is grounded in the principle of generalization error bound, which suggests that, for two models with the same task, if the distance between their training data distributions is smaller, their generalization ability will be closer. We validate the efficacy of TrainProVe across four text-to-image models (Stable Diffusion v1.4, latent consistency model, PixArt-\alpha, and Stable Cascade). The results show that TrainProVe achieves a verification accuracy of over 99% in determining the provenance of suspicious model training data, surpassing all previous methods. Code is available at https://github.com/xieyc99/TrainProVe.
|
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] |
197 |
3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation
|
[
"Weijie Wei",
"Osman Ülger",
"Fatemeh Karimi Nejadasl",
"Theo Gevers",
"Martin R. Oswald"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_3D-AVS_LiDAR-based_3D_Auto-Vocabulary_Segmentation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_3D-AVS_LiDAR-based_3D_Auto-Vocabulary_Segmentation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_3D-AVS_LiDAR-based_3D_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wei_2025_CVPR,
author = {Wei, Weijie and \"Ulger, Osman and Nejadasl, Fatemeh Karimi and Gevers, Theo and Oswald, Martin R.},
title = {3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8910-8920}
}
|
Open-vocabulary segmentation methods offer promising capabilities in detecting unseen object categories, but the category must be aware and needs to be provided by a human, either via a text prompt or pre-labeled datasets, thus limiting their scalability. We propose 3D-AVS, a method for Auto-Vocabulary Segmentation of 3D point clouds for which the vocabulary is unknown and auto-generated for each input at runtime, thus eliminating the human in the loop and typically providing a substantially larger vocabulary for richer annotations. 3D-AVS first recognizes semantic entities from image or point cloud data and then segments all points with the automatically generated vocabulary. Our method incorporates both image-based and point-based recognition, enhancing robustness under challenging lighting conditions where geometric information from LiDAR is especially valuable. Our point-based recognition features a Sparse Masked Attention Pooling (SMAP) module to enrich the diversity of recognized objects. To address the challenges of evaluating unknown vocabularies and avoid annotation biases from label synonyms, hierarchies, or semantic overlaps, we introduce the annotation-free Text-Point Semantic Similarity (TPSS) metric for assessing generated vocabulary quality. Our evaluations on nuScenes and ScanNet200 demonstrate 3D-AVS's ability to generate semantic classes with accurate point-wise segmentations.
|
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] |
198 |
STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
|
[
"Zichen Liu",
"Kunlun Xu",
"Bing Su",
"Xu Zou",
"Yuxin Peng",
"Jiahuan Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_STOP_Integrated_Spatial-Temporal_Dynamic_Prompting_for_Video_Understanding_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_STOP_Integrated_Spatial-Temporal_Dynamic_Prompting_for_Video_Understanding_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_STOP_Integrated_Spatial-Temporal_CVPR_2025_supplemental.pdf
|
2503.15973
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Zichen and Xu, Kunlun and Su, Bing and Zou, Xu and Peng, Yuxin and Zhou, Jiahuan},
title = {STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13776-13786}
}
|
Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging due to limited labeled video data and high training costs. Recent video prompting methods attempt to adapt CLIP for video tasks by introducing learnable prompts, but they typically rely on a single static prompt for all video sequences, overlooking the diverse temporal dynamics and spatial variations that exist across frames. This limitation significantly hinders the model's ability to capture essential temporal information for effective video understanding. To address this, we propose an integrated Spatial-TempOral dynamic Prompting (STOP) model which consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting. Our intra-frame spatial prompts are designed to adaptively highlight discriminative regions within each frame by leveraging intra-frame attention and temporal variation, allowing the model to focus on areas with substantial temporal dynamics and capture fine-grained spatial details. Additionally, to highlight the varying importance of frames for video understanding, we further introduce inter-frame temporal prompts, dynamically inserting prompts between frames with high temporal variance as measured by frame similarity. This enables the model to prioritize key frames and enhances its capacity to understand temporal dependencies across sequences. Extensive experiments on various video benchmarks demonstrate that STOP consistently achieves superior performance against state-of-the-art methods. The code is available at https://github.com/zhoujiahuan1991/CVPR2025-STOP
|
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] |
199 |
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion
|
[
"Haoyue Liu",
"Jinghan Xu",
"Yi Chang",
"Hanyu Zhou",
"Haozhi Zhao",
"Lin Wang",
"Luxin Yan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_TimeTracker_Event-based_Continuous_Point_Tracking_for_Video_Frame_Interpolation_with_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_TimeTracker_Event-based_Continuous_Point_Tracking_for_Video_Frame_Interpolation_with_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_TimeTracker_Event-based_Continuous_CVPR_2025_supplemental.pdf
|
2505.03116
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Haoyue and Xu, Jinghan and Chang, Yi and Zhou, Hanyu and Zhao, Haozhi and Wang, Lin and Yan, Luxin},
title = {TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17649-17659}
}
|
Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods, thanks to the event cameras' advantages, such as high temporal resolution. A hurdle for event-based VFI is how to effectively deal with non-linear motion, caused by the dynamic changes in motion direction and speed within the scene. Existing methods either use events to estimate sparse optical flow or fuse events with image features to estimate dense optical flow. Unfortunately, motion errors often degrade the VFI quality as the continuous motion cues from events do not align with the dense spatial information of images in the temporal dimension. In this paper, we find that object motion is continuous in space, tracking local regions over continuous time enables more accurate identification of spatiotemporal feature correlations. In light of this, we propose a novel continuous point tracking-based VFI framework, named TimeTracker. Specifically, we first design a Scene-Aware Region Segmentation (SARS) module to divide the scene into similar patches. Then, a Continuous Trajectory guided Motion Estimation (CTME) module is proposed to track the continuous motion trajectory of each patch through events. Finally, intermediate frames at any given time are generated through global motion optimization and frame refinement. Moreover, we collect a real-world dataset that features fast non-linear motion. Extensive experiments show that our method outperforms prior arts in both motion estimation and frame interpolation quality.
|
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