20260103_011953/papers.csv

Rank,ID,Title,Authors,Year,Venue,Track,Status,Primary Area,Keywords,Citations,BM25 Score,Combined Score,DOI,URL,PDF,Source,TLDR,Abstract
1,,Video Diffusion Models,Jonathan Ho; Tim Salimans; Alexey Gritsenko; William Chan; Mohammad Norouzi,2022,Neural Information Processing Systems,,,,,2179,0.000,0.000,10.48550/arXiv.2204.03458,https://www.semanticscholar.org/paper/3b2a675bb617ae1a920e8e29d535cdf27826e999,https://arxiv.org/pdf/2204.03458,semantic_scholar,,Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image 
2,,CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer,Zhuoyi Yang; Jiayan Teng; Wendi Zheng; Ming Ding; Shiyu Huang,2024,International Conference on Learning Representations,,,,,1244,0.000,0.000,10.48550/arXiv.2408.06072,https://www.semanticscholar.org/paper/7b248d78573ccf0dca6aa2cec2743d3eccaa9d1a,,semantic_scholar,,"We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels. Previous video generation models often had limited movement and"
3,,Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation,Jay Zhangjie Wu; Yixiao Ge; Xintao Wang; Weixian Lei; Yuchao Gu,2022,IEEE International Conference on Computer Vision,,,,,989,0.000,0.000,10.1109/ICCV51070.2023.00701,https://www.semanticscholar.org/paper/1367dcff4ccb927a5e95c452041288b3f0dd0eff,,semantic_scholar,,"To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator. Despite their promising results, such paradigm is computationally expensive. In this work, we propose a new T2V generation setting—One-Shot Video Tunin"
4,,SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion,Vikram S. Voleti; Chun-Han Yao; Mark Boss; Adam Letts; David Pankratz,2024,European Conference on Computer Vision,,,,,308,0.000,0.000,10.48550/arXiv.2403.12008,https://www.semanticscholar.org/paper/03e2cfa44b64489fb98f09dfbd940043fbef90ad,,semantic_scholar,,"We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, the"
5,,Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation,David Junhao Zhang; Jay Zhangjie Wu; Jia-Wei Liu; Rui Zhao; L. Ran,2023,International Journal of Computer Vision,,,,,286,0.000,0.000,10.1007/s11263-024-02271-9,https://www.semanticscholar.org/paper/a5b7fc1bff0910ff31975ec0a15ed30c41f0a968,,semantic_scholar,,"Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video"
6,,Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation,Shuai Yang; Yifan Zhou; Ziwei Liu; Chen Change Loy,2023,ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia,,,,,285,0.000,0.000,10.1145/3610548.3618160,https://www.semanticscholar.org/paper/1e09b83fe064826a9a1ac61a7bdc00f26be41aee,https://arxiv.org/pdf/2306.07954,semantic_scholar,,"Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video"
7,,Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation,Axel Sauer; Frederic Boesel; Tim Dockhorn; A. Blattmann; Patrick Esser,2024,ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia,,,,,213,0.000,0.000,10.1145/3680528.3687625,https://www.semanticscholar.org/paper/f6ab25605dc36db5e4b4f2f167d905944d5203c4,https://dl.acm.org/doi/pdf/10.1145/3680528.3687625,semantic_scholar,,"Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expen"
8,,FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling,Haonan Qiu; Menghan Xia; Yong Zhang; Yin-Yin He; Xintao Wang,2023,International Conference on Learning Representations,,,,,148,0.000,0.000,10.48550/arXiv.2310.15169,https://www.semanticscholar.org/paper/d831988859f0c077b38094446d8585a8340af223,,semantic_scholar,,"With the availability of large-scale video datasets and the advances of diffusion models, text-driven video generation has achieved substantial progress. However, existing video generation models are typically trained on a limited number of frames, resulting in the inability to generate high-fidelit"
9,,Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion,Shiyuan Yang; Liang Hou; Haibin Huang; Chongyang Ma; Pengfei Wan,2024,International Conference on Computer Graphics and Interactive Techniques,,,,,134,0.000,0.000,10.1145/3641519.3657481,https://www.semanticscholar.org/paper/266cd6516e87580d3a4f9278c794a8896bba5cc1,https://arxiv.org/pdf/2402.03162,semantic_scholar,,"Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for customized video creation. However, current methods lack the focus on separately controlling object motion and camera mo"
10,,Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion,Xun Huang; Zhengqi Li; Guande He; Mingyuan Zhou; Eli Shechtman,2025,arXiv.org,,,,,95,0.000,0.000,10.48550/arXiv.2506.08009,https://www.semanticscholar.org/paper/a8e2e3ff1770fd83228659e9e4d16114ddb9404b,,semantic_scholar,,"We introduce Self Forcing, a novel training paradigm for autoregressive video diffusion models. It addresses the longstanding issue of exposure bias, where models trained on ground-truth context must generate sequences conditioned on their own imperfect outputs during inference. Unlike prior methods"
11,,UniAnimate: taming unified video diffusion models for consistent human image animation,Xiang Wang; Shiwei Zhang; Changxin Gao; Jiayu Wang; Xiaoqiang Zhou,2024,Science China Information Sciences,,,,,75,0.000,0.000,10.1007/s11432-024-4592-3,https://www.semanticscholar.org/paper/6ddedae42ab98883bec0a2259ab65ec71bebc409,,semantic_scholar,,"Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitations: (i) an extra reference model is required to a"
12,,LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation.,Yushi Lan; Fangzhou Hong; Shuai Yang; Shangchen Zhou; Xuyi Meng,2024,IEEE Transactions on Pattern Analysis and Machine Intelligence,,,,,72,0.000,0.000,10.1109/TPAMI.2025.3633073,https://www.semanticscholar.org/paper/3262cda60389d8a58b81f00a5bae7ec87348bccd,,semantic_scholar,,"The field of neural rendering has seen remarkable progress, driven by advancements in generative models and differentiable rendering techniques. While 2D diffusion has achieved notable success, the development of a unified 3D diffusion pipeline remains an open challenge. This paper presents a novel "
13,,Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices,Zhiyuan Ma; Yuzhu Zhang; Guoli Jia; Liangliang Zhao; Yichao Ma,2024,arXiv.org,,,,,30,0.000,0.000,10.48550/arXiv.2410.11795,https://www.semanticscholar.org/paper/140e64cf0516e556e96abe26cc21e4e35f349f36,,semantic_scholar,,"As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and mul"
14,,InstantDrag: Improving Interactivity in Drag-based Image Editing,Joonghyuk Shin; Daehyeon Choi; Jaesik Park,2024,ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia,,,,,25,0.000,0.000,10.1145/3680528.3687668,https://www.semanticscholar.org/paper/8abafadb5367da1a5c8d5476cb2bbcf8a8f241fa,https://dl.acm.org/doi/pdf/10.1145/3680528.3687668,semantic_scholar,,"Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image"
15,,Hierarchical Patch Diffusion Models for High-Resolution Video Generation,Ivan Skorokhodov; Willi Menapace; Aliaksandr Siarohin; S. Tulyakov,2024,Computer Vision and Pattern Recognition,,,,,20,0.000,0.000,10.1109/CVPR52733.2024.00723,https://www.semanticscholar.org/paper/6955476324daf04ceca8bcdea7bcd1b9448999db,https://arxiv.org/pdf/2406.07792,semantic_scholar,,"Diffusion models have demonstrated remarkable performance in image and video synthesis. However, scaling them to high-resolution inputs is challenging and requires restructuring the diffusion pipeline into multiple independent components, limiting scalability and complicating down-stream application"
16,,PLA4D: Pixel-Level Alignments for Text-to-4D Gaussian Splatting,Qiaowei Miao; Yawei Luo; Yi Yang,2024,arXiv.org,,,,,16,0.000,0.000,10.48550/arXiv.2405.19957,https://www.semanticscholar.org/paper/f7cbd52cde8452d12d0f3e6569242a34ceb37625,,semantic_scholar,,"Previous text-to-4D methods have leveraged multiple Score Distillation Sampling (SDS) techniques, combining motion priors from video-based diffusion models (DMs) with geometric priors from multiview DMs to implicitly guide 4D renderings. However, differences in these priors result in conflicting gra"
17,,Fast and Memory-Efficient Video Diffusion Using Streamlined Inference,Zheng Zhan; Yushu Wu; Yifan Gong; Zichong Meng; Zhenglun Kong,2024,Neural Information Processing Systems,,,,,14,0.000,0.000,10.48550/arXiv.2411.01171,https://www.semanticscholar.org/paper/d690dc77e24558c71e00bc4ea25498992a059006,,semantic_scholar,,"The rapid progress in artificial intelligence-generated content (AIGC), especially with diffusion models, has significantly advanced development of high-quality video generation. However, current video diffusion models exhibit demanding computational requirements and high peak memory usage, especial"
18,,SparseDM: Toward Sparse Efficient Diffusion Models,Kafeng Wang; Jianfei Chen; He Li; Zhenpeng Mi; Jun-Jie Zhu,2024,IEEE International Conference on Multimedia and Expo,,,,,14,0.000,0.000,10.1109/ICME59968.2025.11209208,https://www.semanticscholar.org/paper/729d0e15d7b1a8992d9aa94e0434fbb5511a5b33,,semantic_scholar,,"Diffusion models represent a powerful family of generative models widely used for image and video generation. However, the time-consuming deployment, long inference time, and requirements on large memory hinder their applications on resource constrained devices. In this paper, we propose a method ba"
19,,Generative AI Beyond LLMs: System Implications of Multi-Modal Generation,Alicia Golden; Samuel Hsia; Fei Sun; Bilge Acun; Basil Hosmer,2023,IEEE International Symposium on Performance Analysis of Systems and Software,,,,,13,0.000,0.000,10.1109/ISPASS61541.2024.00032,https://www.semanticscholar.org/paper/e1e6dcb3c162a2bbbcb128eff6fed01755613d1c,https://arxiv.org/pdf/2312.14385,semantic_scholar,,"As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency. We present the first work towards understanding t"
20,,MiniMax-Remover: Taming Bad Noise Helps Video Object Removal,Bojia Zi; Weixuan Peng; Xianbiao Qi; Jianan Wang; Shihao Zhao,2025,arXiv.org,,,,,11,0.000,0.000,10.48550/arXiv.2505.24873,https://www.semanticscholar.org/paper/f88b4cb3999cb2cb63d4414b7c046f67b79bdd89,,semantic_scholar,,"Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on compu"
21,,TalkingMachines: Real-Time Audio-Driven FaceTime-Style Video via Autoregressive Diffusion Models,Chetwin Low; Weimin Wang,2025,arXiv.org,,,,,10,0.000,0.000,10.48550/arXiv.2506.03099,https://www.semanticscholar.org/paper/49276c3f739c4ae3f479381b631c0fc955eb5fd4,,semantic_scholar,,"In this paper, we present TalkingMachines -- an efficient framework that transforms pretrained video generation models into real-time, audio-driven character animators. TalkingMachines enables natural conversational experiences by integrating an audio large language model (LLM) with our video genera"
22,,FlightVGM: Efficient Video Generation Model Inference with Online Sparsification and Hybrid Precision on FPGAs,Jun Liu; Shulin Zeng; Li Ding; Widyadewi Soedarmadji; Hao Zhou,2025,Symposium on Field Programmable Gate Arrays,,,,,9,0.000,0.000,10.1145/3706628.3708864,https://www.semanticscholar.org/paper/d88e275bc3cacddab4d2a20c0ac7484b9878a9c7,,semantic_scholar,,"Video Generation Model (VGM), as a representative of multi-modal large models, has revolutionized the productivity of video content creation. VGMs are compute-bound due to adopting the Diffusion Transformer (i.e., DiT) structure. Sparsification is a common method for accelerating compute-intensive m"
23,,EffiVED: Efficient Video Editing via Text-instruction Diffusion Models,Zhenghao Zhang; Zuozhuo Dai; Long Qin; Weizhi Wang,2024,arXiv.org,,,,,9,0.000,0.000,10.48550/arXiv.2403.11568,https://www.semanticscholar.org/paper/b087853373cca366961e648750ad3472c760aa81,,semantic_scholar,,"Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion models or specific inversion optimization to ensure"
24,,FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion,Akide Liu; Zeyu Zhang; Zhexin Li; Xuehai Bai; Yizeng Han,2025,arXiv.org,,,,,8,0.000,0.000,10.48550/arXiv.2506.04648,https://www.semanticscholar.org/paper/6dd7c07555fdd1790ac76d0c5d19f6698b1af5bb,,semantic_scholar,,"Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder practical deployment. Although both quantization and sparsity can independently accelerate inference while maintaining genera"
25,,Magic 1-For-1: Generating One Minute Video Clips within One Minute,Hongwei Yi; Shitong Shao; Tian Ye; Jiantong Zhao; Qingyu Yin,2025,arXiv.org,,,,,7,0.000,0.000,10.48550/arXiv.2502.07701,https://www.semanticscholar.org/paper/0dac8b51df2e94a7623e3aa471058b8f57e9a449,,semantic_scholar,,"In this technical report, we present Magic 1-For-1 (Magic141), an efficient video generation model with optimized memory consumption and inference latency. The key idea is simple: factorize the text-to-video generation task into two separate easier tasks for diffusion step distillation, namely text-"
26,,Optical-Flow Guided Prompt Optimization for Coherent Video Generation,Hyelin Nam; Jaemin Kim; Dohun Lee; Jong Chul Ye,2024,Computer Vision and Pattern Recognition,,,,,7,0.000,0.000,10.1109/CVPR52734.2025.00734,https://www.semanticscholar.org/paper/f63329fc34175c36a63ca3aa4fdb3f28ecd9a8e6,,semantic_scholar,,"While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output quality during inference; however, applying these methods to video"
27,,Optimization-Free Image Immunization Against Diffusion-Based Editing,Tarik Can Ozden; Ozgur Kara; Oguzhan Akcin; Kerem Zaman; Shashank Srivastava,2024,arXiv.org,,,,,6,0.000,0.000,10.48550/arXiv.2411.17957,https://www.semanticscholar.org/paper/14a2e5f5ad0bd5487cb187198f9459b90495f29e,,semantic_scholar,,"Current image immunization defense techniques against diffusion-based editing embed imperceptible noise in target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming re-optimization for each image-taking hours for small batches. To add"
28,,Beyond U: Making Diffusion Models Faster & Lighter,Sergio Calvo-Ordoñez; Jiahao Huang; Lipei Zhang; Guang Yang; C. Schönlieb,2023,arXiv.org,,,,,6,0.000,0.000,10.48550/arXiv.2310.20092,https://www.semanticscholar.org/paper/3b1041c964c30d9d63f7e5bc78ae6595224e4e9c,,semantic_scholar,,
29,,AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset,Haiyu Zhang; Xinyuan Chen; Yaohui Wang; Xihui Liu; Yunhong Wang,2025,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2503.19462,https://www.semanticscholar.org/paper/97317329fab179b38a6580e14b046133a704157d,,semantic_scholar,,"Diffusion models have achieved remarkable progress in the field of video generation. However, their iterative denoising nature requires a large number of inference steps to generate a video, which is slow and computationally expensive. In this paper, we begin with a detailed analysis of the challeng"
30,,The Missing U for Efficient Diffusion Models,Sergio Calvo-Ordoñez; Chun-Wun Cheng; Jiahao Huang; Lipei Zhang; Guang Yang,2023,Trans. Mach. Learn. Res.,,,,,5,0.000,0.000,,https://www.semanticscholar.org/paper/bac74fa84c99d08371f87e156a286c4bb8c8d512,,semantic_scholar,,"Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilit"
31,,LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models,Yu Cheng; Fajie Yuan,2025,arXiv.org,,,,,4,0.000,0.000,10.48550/arXiv.2503.14325,https://www.semanticscholar.org/paper/d4233613baadd347dbb8d8532890bd997572a46d,,semantic_scholar,,"Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space.However, as LVDM training scales, the computational overhead of Video VAEs becomes a cr"
32,,Accelerating Video Diffusion Models via Distribution Matching,Yuanzhi Zhu; Hanshu Yan; Huan Yang; Kai Zhang; Junnan Li,2024,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2412.05899,https://www.semanticscholar.org/paper/a4caae8c11576ff03bbe67634c85ea0da2ae497e,,semantic_scholar,,"Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, current diffusion models are computationally intensive, often requiring numerous sampling steps that limit their practical ap"
33,,Tinker: Diffusion's Gift to 3D-Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization,Canyu Zhao; Xiaoman Li; Tianjian Feng; Zhiyue Zhao; Hao Chen,2025,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2508.14811,https://www.semanticscholar.org/paper/a235b975ff8228731b024c132f6e4a59de9aca06,,semantic_scholar,,"We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edite"
34,,4D Driving Scene Generation With Stereo Forcing,Hao Lu; Zhuang Ma; Guangfeng Jiang; Wenhang Ge; Bohan Li,2025,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2509.20251,https://www.semanticscholar.org/paper/9b5a1f79ba3deb79aa3bfa2a894c3d813a8dbde3,,semantic_scholar,,"Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-scene optimization. Bridging generation and novel view synthesis remains a major challenge. We present PhiGenesis, a unifie"
35,,An Intermediate Fusion ViT Enables Efficient Text-Image Alignment in Diffusion Models,Zizhao Hu; Shaochong Jia; Mohammad Rostami,2024,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2403.16530,https://www.semanticscholar.org/paper/0e2c9745d29a6d689c132ffd624e5ead183b436d,,semantic_scholar,,"Diffusion models have been widely used for conditional data cross-modal generation tasks such as text-to-image and text-to-video. However, state-of-the-art models still fail to align the generated visual concepts with high-level semantics in a language such as object count, spatial relationship, etc"
36,,TITAN-Guide: Taming Inference-Time AligNment for Guided Text-to-Video Diffusion Models,Christian Simon; Masato Ishii; Akio Hayakawa; Zhi-Wei Zhong; Shusuke Takahashi,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2508.00289,https://www.semanticscholar.org/paper/4dae46857270c5e7586d8ed358ab7461da7734c3,,semantic_scholar,,"In the recent development of conditional diffusion models still require heavy supervised fine-tuning for performing control on a category of tasks. Training-free conditioning via guidance with off-the-shelf models is a favorable alternative to avoid further fine-tuning on the base model. However, th"
37,,Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation,Alexey Buzovkin; Evgeny Shilov,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2503.04871,https://www.semanticscholar.org/paper/2f16f8d464395aa0de12f608833618d64e43039a,,semantic_scholar,,We investigate methods to reduce inference time and memory footprint in stable diffusion models by introducing lightweight decoders for both image and video synthesis. Traditional latent diffusion pipelines rely on large Variational Autoencoder decoders that can slow down generation and consume cons
38,,OmnimatteZero: Training-free Real-time Omnimatte with Pre-trained Video Diffusion Models,Dvir Samuel; Matan Levy; Nir Darshan; Gal Chechik; Rami Ben-Ari,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2503.18033,https://www.semanticscholar.org/paper/1563307159e2fa734f23ab1175b2427f0948c9a8,,semantic_scholar,,
39,,OmnimatteZero: Fast Training-free Omnimatte with Pre-trained Video Diffusion Models,Dvir Samuel; Matan Levy; Nir Darshan; Gal Chechik; Rami Ben-Ari,2025,Proceedings of the SIGGRAPH Asia 2025 Conference Papers,,,,,1,0.000,0.000,10.1145/3757377.3763917,https://www.semanticscholar.org/paper/f830df53741cb0c1df4d0d9f809112d27c03b7c3,,semantic_scholar,,"In Omnimatte, one aims to decompose a given video into semantically meaningful layers, including the background and individual objects along with their associated effects, such as shadows and reflections. Existing methods often require extensive training or costly self-supervised optimization. In th"
40,,ConceptVoid: Precision Multi-Concept Erasure in Generative Video Diffusion,Zhongbin Huang; Xingjia Jin; Cunkang Wu; Wei Mao,2025,Mathematics,,,,,1,0.000,0.000,10.3390/math13162652,https://www.semanticscholar.org/paper/53a7739a5298ad88d9c93796546b661848c3709a,,semantic_scholar,,"Generative video diffusion models (GVDs) generate high-fidelity, text-conditioned videos but risk producing unsafe or copyrighted content due to training on large, uncurated datasets. Concept erasure techniques aim to remove such harmful concepts from pre-trained models while preserving overall gene"
41,,Epipolar Geometry Improves Video Generation Models,Orest Kupyn; Fabian Manhardt; Federico Tombari; Christian Rupprecht,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2510.21615,https://www.semanticscholar.org/paper/170a232300d7af70fe361e16c542b37bb56366c2,,semantic_scholar,,"Video generation models have progressed tremendously through large latent diffusion transformers trained with rectified flow techniques. Yet these models still struggle with geometric inconsistencies, unstable motion, and visual artifacts that break the illusion of realistic 3D scenes. 3D-consistent"
42,,Flows and Diffusions on the Neural Manifold,Daniel Saragih; Deyu Cao; Tejas Balaji,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2507.10623,https://www.semanticscholar.org/paper/a909afbb749ae3fed379e4a95f8b408a58cbcec4,,semantic_scholar,,"Diffusion and flow-based generative models have achieved remarkable success in domains such as image synthesis, video generation, and natural language modeling. In this work, we extend these advances to weight space learning by leveraging recent techniques to incorporate structural priors derived fr"
43,,FasterVD: On Acceleration of Video Diffusion Models,Pinrui Yu; Dan Luo; Timothy Rupprecht; Lei Lu; Zhenglun Kong,2024,International Joint Conference on Artificial Intelligence,,,,,1,0.000,0.000,10.24963/ijcai.2024/1044,https://www.semanticscholar.org/paper/4463d38906296308b093eb3f80da32ae3ddc02cb,,semantic_scholar,,"Equipped with Denoising Diffusion Probabilistic Models, video content generation has gained significant research interest recently. However, diffusion pipelines call for intensive computation and model storage, which poses challenges for their wide and efficient deployment. In this work, we address "
44,,SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model,Zhengang Li; Yan Kang; Yuchen Liu; Difan Liu; Tobias Hinz,2024,Computer Vision and Pattern Recognition,,,,,1,0.000,0.000,10.1109/CVPR52733.2024.00827,https://www.semanticscholar.org/paper/ea3a296f97570fd212d6c8fc34bba62f07c50f5c,https://arxiv.org/pdf/2406.00195,semantic_scholar,,"While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality, the complex model architecture and substantial computational demands for both trai"
45,pFyzqbUiF9,Vid2World: Crafting Video Diffusion Models to Interactive World Models,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",World Models;Video Diffusion Models,0,1.000,0.000,,https://openreview.net/forum?id=pFyzqbUiF9,,offline_iclr,,"World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. However, existing world models often require extensive domain-specific training and still produce low-fidelity, coarse pred"
46,ULXYZCms41,Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling,,2026,ICLR 2026,main,Active,generative models,Generative Model; Video Generation; World Modeling,0,0.957,0.000,,https://openreview.net/forum?id=ULXYZCms41,,offline_iclr,,"Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models"
47,CuNHz3zxgm,WorldPack: Compressed Memory Improves Spatial Consistency in Video World Modeling,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",world models;memory;video diffusion models,0,0.916,0.000,,https://openreview.net/forum?id=CuNHz3zxgm,,offline_iclr,,"Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on past observations and navigation actions.
Temporally- and spatially-consistent, long-term world modeling has been a long-standing problem, unresolved with even"
48,5bJZtzTFYy,BWCache: Accelerating Video Diffusion Transformers through Block-Wise Caching,,2026,ICLR 2026,main,Active,generative models,Diffusion Model;Video Generation;Cache,0,0.901,0.000,,https://openreview.net/forum?id=5bJZtzTFYy,,offline_iclr,,"Recent advancements in Diffusion Transformers (DiTs) have established them as the state-of-the-art method for video generation. However, their inherently sequential denoising process results in inevitable latency, limiting real-world applicability. Existing acceleration methods either compromise vis"
49,1y1YFKb9pp,OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling,,2026,ICLR 2026,main,Active,datasets and benchmarks,Multi-Domain;Multi-Modal;World Modeling,0,0.864,0.000,,https://openreview.net/forum?id=1y1YFKb9pp,,offline_iclr,,"The field of 4D world modeling—aiming to jointly capture spatial geometry and temporal dynamics—has witnessed  remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning.  However, the development of truly general 4D world models remains fundamen"
50,guUrm5IRQS,4DNeX: Feed-Forward 4D Generative Modeling Made Easy,Zhaoxi Chen; Tianqi Liu; Long Zhuo; Jiawei Ren; Zeng Tao,2026,ICLR 2026,main,Withdraw,generative models,Image-to-4D Modeling;Generative 4D World Models;4D Dataset,0,0.854,0.000,,https://openreview.net/forum?id=guUrm5IRQS,,offline_iclr,,"We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame video inputs, 4DNeX enables efficient, end-to-end image-to-4D "
51,TTaAacQEdg,MoB: Mixture of Block Transformer for Accelerating Video Generation with Dynamic Routing,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Diffusion Transformer;Video Generation;Efficiency Improvement,0,0.844,0.000,,https://openreview.net/forum?id=TTaAacQEdg,,offline_iclr,,"Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation tasks. However, their iterative denoising process introduces substantial computational redundancy within Transformer modules, resulting in prohibitively high computational costs and sl"
52,n5dkjiplv4,Reinforcement Learning with Inverse Rewards for World Model Post-training,,2026,ICLR 2026,main,Active,generative models,world model;reinforcement learning;post-training,0,0.843,0.000,,https://openreview.net/forum?id=n5dkjiplv4,,offline_iclr,,"World models simulate dynamic environments, enabling agents to interact with diverse input modalities. Although recent advances have improved the visual quality and temporal consistency of video world models, their ability of accurately modeling human-specified actions remains underexplored. Reinfor"
53,e8P4Oo8S6U,ASTRAEA: A Token-wise Acceleration Framework for Video Diffusion Transformers,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",video diffusion acceleration,0,0.836,0.000,,https://openreview.net/forum?id=e8P4Oo8S6U,,offline_iclr,,"Video diffusion transformers (vDiTs) have made tremendous progress in text-to-video generation, but their high computational demands pose a major challenge for practical deployment. While existing studies propose acceleration methods to reduce workload at various granularities, they often rely on he"
54,V66pMNOVC2,CVD-STORM: Cross-View Video Diffusion with Spatial-Temporal Reconstruction Model for Autonomous Driving,Tianrui ZHANG; Yichen Liu; Zilin Guo; Yuxin Guo; Jingcheng Ni,2026,ICLR 2026,main,Withdraw,generative models,generative model;world model,0,0.831,0.000,,https://openreview.net/forum?id=V66pMNOVC2,,offline_iclr,,"Generative models have been widely applied to world modeling for environment simulation and future state prediction. With advancements in autonomous driving, there is a growing demand not only for high-fidelity video generation under various controls, but also for producing diverse and meaningful in"
55,wPEIStHxYH,Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",world models;robotics;manipulation;model-based planning;imitation learning;video generation,0,0.831,0.000,,https://openreview.net/forum?id=wPEIStHxYH,,offline_iclr,,"Recent video generation models demonstrate remarkable ability to capture complex physical interactions and scene evolution over time. To leverage their spatiotemporal priors, robotics works have adapted video models for policy learning but introduce complexity by requiring multiple stages of post-tr"
56,2uNlM353RI,Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency,,2026,ICLR 2026,main,Active,generative models,Diffusion Models;Distillation;Consistency Models;Few-Step Generation,0,0.824,0.000,,https://openreview.net/forum?id=2uNlM353RI,,offline_iclr,,"This work represents the first effort to scale up continuous-time consistency distillation to general application-level image and video diffusion models. Although continuous-time consistency model (sCM) is theoretically principled and empirically powerful for accelerating academic-scale diffusion, i"
57,LFCSVVIy1x,Can World Models Benefit VLMs for World Dynamics?,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",World Model;Multi-modal Large Language Model;Multi-modal Representation Learning,0,0.818,0.000,,https://openreview.net/forum?id=LFCSVVIy1x,,offline_iclr,,"Trained on internet-scale video data, world models are increasingly recognized as powerful world simulators that can generate consistent and plausible dynamics over structure, motion, and physics. While recent studies have explored the few-shot learning capabilities of world models on vision tasks, "
58,76flfkkawe,LongScape: Advancing Long-Horizon Embodied World Models with Context-Aware MoE,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",embodied world model;long-horizon video generation,0,0.814,0.000,,https://openreview.net/forum?id=76flfkkawe,,offline_iclr,,"Video-based world models hold significant potential for generating high-quality embodied manipulation data. However, current video generation methods struggle to achieve stable long-horizon generation: classical diffusion-based approaches often suffer from temporal inconsistency and visual drift ove"
59,c2EfS9E5CJ,AdaViewPlanner: Adapting Video Diffusion Models for Viewpoint Planning in 4D Scenes,,2026,ICLR 2026,main,Active,generative models,Viewpoint Planning in 4D Scenes; Video Model,0,0.808,0.000,,https://openreview.net/forum?id=c2EfS9E5CJ,,offline_iclr,,"Recent Text-to-Video (T2V) models have demonstrated powerful capability in visual simulation of real-world geometry and physical laws, indicating its potential as implicit world models. Inspired by this, we explore the feasibility of leveraging the video generation prior for viewpoint planning from "
60,rCNe4N7cP4,X-Streamer: Unified Human World Modeling with Audiovisual Interaction,,2026,ICLR 2026,main,Active,generative models,Portrait Animation; Diffusion Forcing; Generative Models; Large Language Models;,0,0.804,0.000,,https://openreview.net/forum?id=rCNe4N7cP4,,offline_iclr,,"We introduce X-Streamer, an end-to-end multimodal human world modeling framework for building digital human agents capable of infinite interactions across text, speech, and video within a single unified architecture. Starting from a single portrait, X-Streamer enables real-time, open-ended video cal"
61,RFrc0g7pSu,PAV-DiT: A Cross-modal Alignment Projected Latent Diffusion Transformer for Synchronized Audio-Video Generation,Jiahui Sun; Weining Wang; Mingzhen Sun; Yirong Yang; Xinxin Zhu,2026,ICLR 2026,main,Withdraw,generative models,video generation;diffusion model;diffusion transformer;sounding video generation,0,0.799,0.000,,https://openreview.net/forum?id=RFrc0g7pSu,,offline_iclr,,"Sounding video generation (SVG) has emerged as a challenging task due to the inherent cross-modal temporal and semantic misalignment and the high computational costs associated with multimodal data. To address these issues, we propose the Projected Latent Audio-Video Diffusion Transformer (PAV-DiT),"
62,mzAchylAtf,SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer,,2026,ICLR 2026,main,Active,generative models,Video Diffusion Model,0,0.795,0.000,,https://openreview.net/forum?id=mzAchylAtf,,offline_iclr,,"We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720×1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Tw"
63,8UZpmrxoLG,Astra: General Interactive World Model with Autoregressive Denoising,,2026,ICLR 2026,main,Active,generative models,world model;video generation,0,0.795,0.000,,https://openreview.net/forum?id=8UZpmrxoLG,,offline_iclr,,"Recent advances in diffusion transformers have empowered video generation models to generate high-quality video clips from texts or images. However, world models with the ability to predict long-horizon futures from past observations and actions remain underexplored, especially for general-purpose s"
64,E0ZAcqy9TB,Video-GPT via Next Clip Diffusion,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Video; Diffusion; LLM,0,0.793,0.000,,https://openreview.net/forum?id=E0ZAcqy9TB,,offline_iclr,,"GPT has shown its remarkable success in natural language processing. However, the language sequence is not sufficient to describe spatial-temporal details in the visual world. Alternatively, the video sequence is good at capturing such details. Motivated by this fact, we propose a concise Video-GPT "
65,k8KIwW4f7P,BlockVid: Block Diffusion for High-Fidelity and Coherent Minute-Long Video Generation,Zeyu Zhang; Shuning Chang; Yuanyu He; Yizeng Han; Jiasheng Tang,2026,ICLR 2026,main,Withdraw,generative models,Long Video Generation;Block Diffusion,0,0.788,0.000,,https://openreview.net/forum?id=k8KIwW4f7P,,offline_iclr,,"Generating minute-long videos is a critical step toward developing world models, providing a foundation for realistic extended scenes and advanced AI simulators. The emerging semi-autoregressive (block diffusion) paradigm integrates the strengths of diffusion and autoregressive models, enabling arbi"
66,vrY91av397,Learning to Generate Object Interactions with Physics-Guided Video Diffusion,David Romero; Ariana Bermudez; Hao Li; Fabio Pizzati; Ivan Laptev,2026,ICLR 2026,main,Withdraw,generative models,Video Diffusion;Diffusion Models;Physics;Velocity,0,0.784,0.000,,https://openreview.net/forum?id=vrY91av397,,offline_iclr,,"Recent models for video generation have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and embodied decision making. Despite strong advances, however,"
67,6dyJj5a6FW,Accelerating Discrete Diffusion Models with Parallel Sampling,,2026,ICLR 2026,main,Active,generative models,Generative Models;Discrete Diffusion models;Parallel Computing;Sampling,0,0.779,0.000,,https://openreview.net/forum?id=6dyJj5a6FW,,offline_iclr,,"Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of significant importance. In this work, we parallelize the mainstream $\tau$-leaping algorithm for absorbing discrete dif"
68,seyWxIzcAn,Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion,Hau-Shiang Shiu; Chin-Yang Lin; Zhixiang Wang; Chi-Wei Hsiao; Po-Fan Yu,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Video Super-Resolution;Online Video Restoration;Video restoration;Diffusion models,0,0.779,0.000,,https://openreview.net/forum?id=seyWxIzcAn,,offline_iclr,,"Diffusion-based video super-resolution (VSR) methods have recently demonstrated remarkable perceptual quality; however, their reliance on future-frame information and computationally expensive iterative denoising has restricted their application in latency-sensitive contexts. We present Stream-DiffV"
69,6ekx5J6mnK,DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder,,2026,ICLR 2026,main,Active,generative models,efficient video diffusion model;deep compression video autoencoder;post-training model acceleration,0,0.778,0.000,,https://openreview.net/forum?id=6ekx5J6mnK,,offline_iclr,,"We introduce DC-VideoGen, a post-training acceleration framework for efficient video generation. DC-VideoGen can be applied to any pre-trained video diffusion model, improving efficiency by adapting it to a deep compression latent space with lightweight fine-tuning. The framework builds on two key i"
70,vko4DuhKbh,Flow Caching for Autoregressive Video Generation,,2026,ICLR 2026,main,Active,generative models,Autoregressive video generation;chunkwise caching;KV cache compression;ultra-long video synthesis;video acceleration,0,0.778,0.000,,https://openreview.net/forum?id=vko4DuhKbh,,offline_iclr,,"Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. However, this sequential generation process is notoriously slow. While caching strategies have proven effective for accelerati"
71,nRpWcxwYvC,SRDiffusion: Accelerate Diffusion Inference via Sketching-Rendering Cooperation,,2026,ICLR 2026,main,Active,"infrastructure, software libraries, hardware, systems, etc.",video generation;diffusion models;inference acceleration,0,0.768,0.000,,https://openreview.net/forum?id=nRpWcxwYvC,,offline_iclr,,"Leveraging the diffusion transformer (DiT) architecture, models like Sora, CogVideoX and Wan have achieved remarkable progress in text-to-video, image-to-video, and video editing tasks. Despite these advances, diffusion-based video generation remains computationally intensive, especially for high-re"
72,WyfmWX2ncn,SemCache:Adaptive Semantic-Aware Caching for Efficient Video Diffusion,,2026,ICLR 2026,main,Active,generative models,Video diffusion model acceleration ;Video generation ;Diffusion transformers,0,0.750,0.000,,https://openreview.net/forum?id=WyfmWX2ncn,,offline_iclr,,"Diffusion models have achieved significant progress in video generation tasks, but slow inference speed remains a major challenge. Existing cache-based acceleration methods for video diffusion have demonstrated considerable improvements in inference speed. An existing efficient caching strategy invo"
73,NomB0oiwqI,"LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation",,2026,ICLR 2026,main,Active,optimization,Cache;Training-free,0,0.725,0.000,,https://openreview.net/forum?id=NomB0oiwqI,,offline_iclr,,"Training-free acceleration has emerged as an advanced research area in video generation. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. We decompose the inference process into the encoding, denoising, and decoding stages, and observe that cach"
74,td24jA8Kui,TraCache: Trajectory-Aware Feature Prediction for Training-Free Diffusion Transformer Acceleration,,2026,ICLR 2026,main,Active,generative models,Diffusion Transformers;Inference Acceleration;Feature Caching,0,0.721,0.000,,https://openreview.net/forum?id=td24jA8Kui,,offline_iclr,,"Diffusion transformers have achieved remarkable success across various generative tasks but suffer from high inference costs. A promising line of work addresses this by reusing features across timesteps to minimize computational redundancy. However, existing methods degrade quality as temporal gaps "
75,qccGDaGCtz,Asymmetric VAE for One-Step Video Super-Resolution Acceleration,Jianze Li; Yong Guo; Yulun Zhang; Xiaokang Yang,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Video Super-Resolution,0,0.695,0.000,,https://openreview.net/forum?id=qccGDaGCtz,,offline_iclr,,"Diffusion models have significant advantages in the field of real-world video super-resolution and have demonstrated strong performance in past research. In recent diffusion-based video super-resolution (VSR) models, the number of sampling steps has been reduced to just one, yet there remains signif"
76,uXmbrTlko7,ScalingCache: Extreme Acceleration of DiTs through Difference Scaling and Dynamic Interval Caching,,2026,ICLR 2026,main,Active,generative models,​​Diffusion Transformer;Image generation;Video generation;Model Acceleration;Feature Cache,0,0.691,0.000,,https://openreview.net/forum?id=uXmbrTlko7,,offline_iclr,,"Diffusion Transformers (DiTs) have emerged as powerful generative models, but their iterative denoising structure and deep transformer blocks incur substantial computational overhead, limiting the accessibility and practical deployment of high-quality video generation. To address this bottleneck, we"
77,EQLcuMSy8b,ROPA : Robust parallel diffusion sampling,,2026,ICLR 2026,main,Active,generative models,Generative models;Parallel diffusion sampling,0,0.688,0.000,,https://openreview.net/forum?id=EQLcuMSy8b,,offline_iclr,,"Recent years have witnessed significant progress in developing effective diffusion models. Parallel sampling is a promising recent approach that reformulates the sequential denoising process as solving a system of nonlinear equations, and it can be combined with other acceleration techniques. Howeve"
78,jXZZbraJpG,Playing with Transformer at 30+ FPS via Next-Frame Diffusion,Xinle Cheng; Tianyu He; Jiayi Xu; Junliang Guo; Di He,2026,ICLR 2026,main,Withdraw,generative models,video generation;autoregressive models;diffusion models,0,0.680,0.000,,https://openreview.net/forum?id=jXZZbraJpG,,offline_iclr,,"Autoregressive video models offer distinct advantages over bidirectional diffusion models in creating interactive video content and supporting streaming applications with arbitrary duration.  Nonetheless, achieving real-time video generation remains a significant challenge for such models, primarily"
79,ojOkkp22Fa,Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs,Hyungjin Chung; Hyelin Nam; Jiyeon Kim; Hyojun Go; Byeongjun Park,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",VideoLLM;Scaling Law;Parallel Scaling;Multimodal Understanding,0,0.679,0.000,,https://openreview.net/forum?id=ojOkkp22Fa,,offline_iclr,,"Video Large Language Models (VideoLLMs) face a critical bottleneck: increasing the number of input frames to capture fine-grained temporal detail leads to prohibitive computational costs and performance degradation from long context lengths. We introduce Video Parallel Scaling (VPS), an inference-ti"
80,eD8IPvNoZB,SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse–Linear Attention,,2026,ICLR 2026,main,Active,generative models,sparse attention;efficient attention;video diffusion model;video generation;diffusion transformer,0,0.678,0.000,,https://openreview.net/forum?id=eD8IPvNoZB,,offline_iclr,,"In Diffusion Transformer (DiT) models, particularly for video generation, attention latency is a major bottleneck due to the long sequence length and the quadratic complexity. Interestingly, we find that attention weights can be decoupled into two matrices: a small fraction of large weights with hig"
81,faYbbo1KsQ,HiCache: A Plug-in Scaled-Hermite Upgrade for Taylor-Style Cache-then-Forecast Diffusion Acceleration,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Diffusion Acceleration;Efficiency ML,0,0.672,0.000,,https://openreview.net/forum?id=faYbbo1KsQ,,offline_iclr,,"Diffusion models have achieved remarkable success in content generation but suffer from prohibitive computational costs due to iterative sampling. While recent feature caching methods tend to accelerate inference through temporal extrapolation, these methods still suffer from severe quality loss due"
82,pt4iKnAm0M,Plug-and-Play Fidelity Optimization for Diffusion Transformer Acceleration via Cumulative Error Minimization,,2026,ICLR 2026,main,Active,generative models,Training-free acceleration;Diffusion transformer;Error correction,0,0.664,0.000,,https://openreview.net/forum?id=pt4iKnAm0M,,offline_iclr,,"Although Diffusion Transformer (DiT) has emerged as a predominant architecture for image and video generation, its iterative denoising process results in slow inference, which hinders broader applicability and development. Caching-based methods achieve training-free acceleration, while suffering fro"
83,kflYZjGumW,DiCache: Let Diffusion Model Determine Its Own Cache,,2026,ICLR 2026,main,Active,generative models,diffusion model;generative model;inference acceleration,0,0.662,0.000,,https://openreview.net/forum?id=kflYZjGumW,,offline_iclr,,"Recent years have witnessed the rapid development of acceleration techniques for diffusion models, especially caching-based acceleration methods. These studies seek to answer two fundamental questions: _""When to cache""_ and _""How to use cache""_, typically relying on predefined empirical laws or data"
84,NLsUsrOIuh,Compact Attention: Exploiting Structured Spatio-Temporal Sparsity for Fast Video Generation,,2026,ICLR 2026,main,Active,generative models,Sparse Attention;Diffusion Models,0,0.658,0.000,,https://openreview.net/forum?id=NLsUsrOIuh,,offline_iclr,,"The quadratic computational complexity of self-attention mechanisms pose a critical challenge for transformer-based video generation in synthesizing ultra-long sequences. 
 Current sparse approaches with fixed patterns fail to fully exploit the inherent spatio-temporal redundancies in video data. Th"
85,InvyBiYcK5,ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion,,2026,ICLR 2026,main,Active,generative models,Diffusion,0,0.654,0.000,,https://openreview.net/forum?id=InvyBiYcK5,,offline_iclr,,"Diffusion models suffer from substantial computational overhead due to their inherently iterative inference process. While feature caching offers a promising acceleration strategy by reusing intermediate outputs across timesteps, naive reuse often incurs noticeable quality degradation. 
In this work"
86,pjaE5QaFbm,ETC: training-free diffusion models acceleration with Error-aware Trend Consistency,,2026,ICLR 2026,main,Active,generative models,Diffusion model;Training-free acceleration,0,0.651,0.000,,https://openreview.net/forum?id=pjaE5QaFbm,,offline_iclr,,"Diffusion models have achieved remarkable generative quality but remain bottlenecked by costly iterative sampling. Recent training-free methods accelerate diffusion process by reusing model outputs. However, these methods ignore denoising trends and lack error control for model-specific tolerance, l"
87,O9J20MsmRl,BLADE: Block-Sparse Attention Meets Step Distillation for Efficient Video Generation,,2026,ICLR 2026,main,Active,generative models,sparse attention; video generation; step distillation,0,0.633,0.000,,https://openreview.net/forum?id=O9J20MsmRl,,offline_iclr,,"Diffusion transformers currently lead the field in high-quality video generation, but their slow iterative denoising process and prohibitive quadratic attention costs for long sequences create significant inference bottlenecks. While both step distillation and sparse attention mechanisms have shown "
88,X7YW6STzeL,Streaming Autoregressive Video Generation via Diagonal Distillation,,2026,ICLR 2026,main,Active,generative models,Video Generation;Diffusion Models,0,0.629,0.000,,https://openreview.net/forum?id=X7YW6STzeL,,offline_iclr,,"Large-scale pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limited. Autoregressive models offer a natural framework for sequential frame synthesis but require heavy computation to achieve high fidelity. Diffus"
89,U2SJE6W3wT,Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Real-World Video Super-Resolution;One-Step Diffusion;Improved Adversarial Diffusion Compression;Diffusion Distillation,0,0.628,0.000,,https://openreview.net/forum?id=U2SJE6W3wT,,offline_iclr,,"While many diffusion models have achieved impressive results in real-world video super-resolution (Real-VSR) by generating rich and realistic details, their reliance on multi-step sampling leads to slow inference. One-step networks like SeedVR2, DOVE, and DLoRAL alleviate this through condensing gen"
90,N5RV691l3H,Reward-Guided Trajectory Distillation for Accelerated Diffusion-Based Video Generation,Zhefan Rao; Qifeng Chen; Harry Yang; Ser-Nam Lim,2026,ICLR 2026,main,Withdraw,generative models,video generation; distillation; reward model,0,0.627,0.000,,https://openreview.net/forum?id=N5RV691l3H,,offline_iclr,,"Recent advancements in video generation models have achieved remarkable quality but often suffer from slow inference due to the iterative denoising processes required by diffusion models. In this paper, we propose a novel distillation pipeline that leverages a reward model to improve the performance"
91,JCujsFnDS7,Whisfusion: Parallel ASR Decoding via a Diffusion Transformer,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Automatic Speech Recognition(ASR);Non-Autoregressive Models;Diffusion Transformers;Whisper;Speech-to-Text,0,0.627,0.000,,https://openreview.net/forum?id=JCujsFnDS7,,offline_iclr,,"Fast Automatic Speech Recognition (ASR) is critical for real-time applications such as captioning and transcription. However, truly parallel ASR decoding remains challenging due to the sequential nature of autoregressive (AR) decoders and the context limitations of non-autoregressive (NAR) methods. "
92,jUNmW3s45i,DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance,Xuan Shen; Chenxia Han; Yufa Zhou; Yanyue Xie; Yifan Gong,2026,ICLR 2026,main,Withdraw,generative models,Video Generation;Efficient Video Generation;Sparse Attention,0,0.622,0.000,,https://openreview.net/forum?id=jUNmW3s45i,,offline_iclr,,"Video generation models based on diffusion transformers have recently attracted widespread attention for their excellent generation quality.
Despite recent progress, their computational expense remains the principal bottleneck. In particular, attention alone accounts for more than 80\% of the overal"
93,fpQpQbFPCU,Generative View Stitching,,2026,ICLR 2026,main,Active,generative models,Video Generation;Camera-guided Video Generation;Video Diffusion Models,0,0.618,0.000,,https://openreview.net/forum?id=fpQpQbFPCU,,offline_iclr,,"Autoregressive video diffusion models are capable of extremely long rollouts that are stable and consistent with history, but they are unable to guide the current generation with conditioning from the future. In camera-guided video generation with a predefined camera trajectory, this limitation lead"
94,OiWyf1BNtC,Realtime Video Frame Interpolation using One-Step Diffusion Sampling,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Video Frame Interpolation; Diffusion Models; Realtime Processing,0,0.618,0.000,,https://openreview.net/forum?id=OiWyf1BNtC,,offline_iclr,,"Recent research on video Frame Interpolation (VFI) shows that a pretrained  Video Diffusion Model (VDM) can solve many challenging scenarios, including large or complex motion. However, VDMs require tedious diffusion sampling, making the inference slow. One possible way to accelerate is to distill a"
95,4TAG3aQljJ,QuantSparse: Comprehensively Compressing Video Diffusion Transformer with Model Quantization and Attention Sparsification,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Video Generation;Model Quantization;Attention Sparsification,0,0.615,0.000,,https://openreview.net/forum?id=4TAG3aQljJ,,offline_iclr,,"Diffusion transformers exhibit remarkable video generation capability, yet their prohibitive computational and memory costs hinder practical deployment. Model quantization and attention sparsification are two promising directions for compression, but each alone suffers severe performance degradation"
96,y39XbEp1vK,Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Model,,2026,ICLR 2026,main,Active,generative models,controllable video generation;training-free guidance;video diffusion models,0,0.601,0.000,,https://openreview.net/forum?id=y39XbEp1vK,,offline_iclr,,"Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes increasingly impractical as model sizes continue to grow. I"
97,URbsHlTK8c,Let Features Decide Their Own Solvers: Hybrid Feature Caching for Diffusion Transformers,,2026,ICLR 2026,main,Active,generative models,Efficient ML;Diffusion Transformer Acceleration;Feature Caching,0,0.600,0.000,,https://openreview.net/forum?id=URbsHlTK8c,,offline_iclr,,"Diffusion Transformers (DiTs) offer state-of-the-art fidelity in image and video synthesis, but their iterative sampling process remains a major bottleneck due to the high cost of transformer forward passes at each timestep. To mitigate this, feature caching has emerged as a training-free accelerati"
98,rKJ7A30lQQ,Self Speculative Decoding for Diffusion Large Language Model,Yifeng Gao; Ziang Ji; Yuxuan Wang; Biqing Qi; Hanlin xu,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",LLM;DLLM;Speculative Decoding,0,0.599,0.000,,https://openreview.net/forum?id=rKJ7A30lQQ,,offline_iclr,,"Diffusion-based Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive models, offering unique advantages through bidirectional attention mechanisms and iterative denoising processes. However, their practical deployment is hindered by high inference latency, particul"
99,sQ0g6EkpF7,RAPID$^3$: Tri-Level Reinforced Acceleration Policies for Diffusion Transformer,,2026,ICLR 2026,main,Active,generative models,Diffusion Transformer;Acceleration,0,0.596,0.000,,https://openreview.net/forum?id=sQ0g6EkpF7,,offline_iclr,,"Diffusion Transformers (DiTs) excel at visual generation yet remain hampered by slow sampling.  Existing training-free accelerators—step reduction, feature caching, and sparse attention—enhance inference speed but typically rely on a uniform heuristic or manually designed adaptive strategy for all i"
100,SdnkB5pGbq,Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",dLLMs;Inference Acceleration,0,0.595,0.000,,https://openreview.net/forum?id=SdnkB5pGbq,,offline_iclr,,"Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms. However, their computational complexity, scaling as $\mathcal{O}(L^3)$ with sequence length $L$, poses significant chall"
101,wWAxwSCKR2,Lumos-1: On Autoregressive Video Generation with Discrete Diffusion from a Unified Model Perspective,,2026,ICLR 2026,main,Active,generative models,Video generation;autoregressive models,0,0.590,0.000,,https://openreview.net/forum?id=wWAxwSCKR2,,offline_iclr,,"Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive (AR) video generation. Existing AR video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive la"
102,c6ZWfQLOWD,Block-wise Adaptive Caching for Accelerating Diffusion Policy,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",Efficient AI;Diffusion Policy;Visuomotor Policy;Robotics;Action Generation;Model Caching.,0,0.579,0.000,,https://openreview.net/forum?id=c6ZWfQLOWD,,offline_iclr,,"Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control.
Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Polic"
103,rQM3oU9cyg,Accelerating Discrete Diffusion Decoding with Parallel Scan,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Efficient AI System;Discrete Diffusion Model,0,0.559,0.000,,https://openreview.net/forum?id=rQM3oU9cyg,,offline_iclr,,"Diffusion-based language models provide strong controllability and parallel generation capabilities, but suffer from prohibitively high decoding cost. Block diffusion, a semi-autoregressive approach, alleviates this issue by reducing diffusion steps and enabling KVCache utilization, yet it restricts"
104,FmV289gGHp,ParaSolver-Turbo: Accelerating Parallel Diffusion Integrator via Intrinsic Partially Linear Structure,,2026,ICLR 2026,main,Active,generative models,Diffusion Models,0,0.558,0.000,,https://openreview.net/forum?id=FmV289gGHp,,offline_iclr,,"This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic system perspective, in which the inherent sequential nature is transformed into a parallel sampling process. 
Specifically, we fir"
105,SJ2WK6kdPJ,NABLA: Neighborhood-Adaptive Block-Level Attention for Efficient Video Diffusion Transformers,,2026,ICLR 2026,main,Active,generative models,Video generation;diffusion transformers;sparse attention;adaptive computation,0,0.558,0.000,,https://openreview.net/forum?id=SJ2WK6kdPJ,,offline_iclr,,"Full self‑attention in video diffusion transformers scales quadratically with the spatio‑temporal token count, making processing the high‑resolution clips prohibitively slow and memory‑heavy. We introduce NABLA, a Neighborhood‑Adaptive Block‑Level Attention mechanism that builds a per‑head sparse ma"
106,Xs7DhA88bd,Synergistic Absorption-Diffusion: Dual-branch Enhanced Continuous-Time Modeling for Parallel Token Generation,,2026,ICLR 2026,main,Active,generative models,Diffusion Language Models;Text Generation,0,0.554,0.000,,https://openreview.net/forum?id=Xs7DhA88bd,,offline_iclr,,"Recent advancements in diffusion models, such as global optimization and parallel token prediction, have enhanced global consistency compared to autoregressive Transformers. However, existing diffusion models exhibit unfavorable trade-offs between efficiency and quality, in which the multi-step iter"
107,bFJ8Sdr224,Learning to Parallel: Accelerating Diffusion Large Language Models via Adaptive Parallel Decoding,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Diffusion LLM,0,0.551,0.000,,https://openreview.net/forum?id=bFJ8Sdr224,,offline_iclr,,"Autoregressive decoding in large language models (LLMs) requires $\mathcal{O}(n)$ sequential steps for $n$ tokens, fundamentally limiting inference throughput. Recent diffusion-based LLMs (dLLMs) enable parallel token generation through iterative denoising. However, current parallel decoding strateg"
108,3Z3Is6hnOT,Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Diffusion LLM;efficiency,0,0.550,0.000,,https://openreview.net/forum?id=3Z3Is6hnOT,,offline_iclr,,"Diffusion-based large language models (Diffusion LLMs) have shown promise for non-autoregressive text generation. However, the practical inference speed of open-sourced Diffusion LLMs often lags behind autoregressive models due to the lack of Key-Value (KV) Cache and quality degradation when decodin"
109,XjcHRIu0iF,Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing,,2026,ICLR 2026,main,Active,generative models,masked diffusion models;language models;inference,0,0.544,0.000,,https://openreview.net/forum?id=XjcHRIu0iF,,offline_iclr,,"Masked diffusion models (MDMs) offer a compelling alternative to autoregres-
sive models (ARMs) for discrete text generation because they enable parallel
token sampling, rather than sequential, left-to-right generation. This means po-
tentially much faster inference. However, effective parallel samp"
110,OsZr5T7Cd0,ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",diffusion LLMs;parallel decoding;benchmark,0,0.543,0.000,,https://openreview.net/forum?id=OsZr5T7Cd0,,offline_iclr,,"While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inference through parallel decoding. Despite this promise, the conditional independence assumption in dLLMs causes parallel dec"
111,mkQAd11ovn,Parallel Multimodal Diffusion Language Models for Thinking-Aware Editing and Generation,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",generation;multimodal diffusion language model,0,0.539,0.000,,https://openreview.net/forum?id=mkQAd11ovn,,offline_iclr,,"While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. 
To systematically analyze this issue, we propose ParaBench, a new be"
112,5bkAbueJwM,Diffusion Language Models are Provably Optimal Parallel Samplers,,2026,ICLR 2026,main,Active,learning theory,Theory;Diffusion Language Model;Large Language Model,0,0.532,0.000,,https://openreview.net/forum?id=5bkAbueJwM,,offline_iclr,,Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundation for this advantage by formalizing a model of parallel sampling and showing that DLMs augmented with polynomial-length
113,15ASUbzg0N,AVID: Adapting Video Diffusion Models to World Models,Marc Rigter; Tarun Gupta; Agrin Hilmkil; Chao Ma,2025,ICLR 2025,main,Reject,"applications to computer vision, audio, language, and other modalities",world models;video diffusion;black box adaptation;controllable video generation,0,0.978,0.000,,https://openreview.net/forum?id=15ASUbzg0N,,offline_iclr,,"Large-scale generative models have achieved remarkable success in a number of domains. However, for sequential decision-making problems, such as robotics, action-labelled data is often scarce and therefore scaling-up foundation models for decision-making remains a challenge. A potential solution lie"
114,EIEVNiraPS,Spiral Evolution of Visual World Model: Reclaiming Autoregression from the Diffusion Era,Wei Yu; Runjia Qian; Songheng Yin; Animesh Garg,2025,NIPS 2025,Position,Reject,,Interactive World Model; Autoregressive Prediction; Video Generation; Multimodal-Controlled Video Generation,0,0.939,0.000,,https://openreview.net/forum?id=EIEVNiraPS,,offline_nips,,"Recent advances in video generation have been dominated by diffusion-based models, which produce high-quality, prompt-faithful sequences through holistic denoising. While this paradigm has achieved striking visual fidelity, it falls short for real-time, interactive applications that require frame-le"
115,SIZhZrU41O,Video Diffusion Models Learn the Structure of the Dynamic World,Zhipeng Bao; Anurag Bagchi; Yu-Xiong Wang; Pavel Tokmakov; Martial Hebert,2025,ICLR 2025,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Diffusion Models;Video Understanding;Representation Learning,0,0.917,0.000,,https://openreview.net/forum?id=SIZhZrU41O,,offline_iclr,,"Diffusion models have demonstrated significant progress in visual perception tasks due to their ability to capture fine-grained, object-centric features through large-scale vision-language pretraining. While their success in image-based tasks is well-established, extending this capability to the dom"
116,yYZbZGo4ei,Accelerating Diffusion Transformers with Token-wise Feature Caching,Chang Zou; Xuyang Liu; Ting Liu; Siteng Huang; Linfeng Zhang,2025,ICLR 2025,main,Poster,generative models,Diffusion Models;Image generation;Video generation;Model Acceleration;Feature Cache,0,0.873,0.000,,https://iclr.cc/virtual/2025/poster/27718,https://openreview.net/pdf?id=yYZbZGo4ei,offline_iclr,,"Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching methods have been introduced to accelerate diffusion transformers by caching the features in previous timesteps and reusing"
117,7VLxvVEtHh,PanoWan: Lifting Diffusion Video Generation Models to 360$^\circ$ with Latitude/Longitude-aware Mechanisms,Yifei Xia; Shuchen Weng; Siqi Yang; Jingqi Liu; Chengxuan Zhu,2025,NIPS 2025,main,Poster,deep_learning,Panoramic Video Generation;Video Diffusion Models,0,0.840,0.000,,https://openreview.net/forum?id=7VLxvVEtHh,,offline_nips,,"Panoramic video generation enables immersive 360$^\circ$ content creation, valuable in applications that demand scene-consistent world exploration. However, existing panoramic video generation models struggle to leverage pre-trained generative priors from conventional text-to-video models for high-q"
118,FzfYoUp8F1,Learning World Models for Interactive Video Generation,Taiye Chen; Xun Hu; Zihan Ding; Chi Jin,2025,NIPS 2025,main,Poster,deep_learning,world model;diffusion model;video generation,0,0.837,0.000,,https://openreview.net/forum?id=FzfYoUp8F1,,offline_nips,,"Foundational world models must be both interactive and preserve spatialtemporal coherence to enable effective future planning with different action choices. However, present models for long video generation have limited inherent world modeling capabilities due to two main challenges: compounding err"
119,33624,MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction,Jingcheng Ni; Yuxin Guo; Yichen Liu; Rui Chen; Lewei Lu,2025,CVPR 2025,main,Poster,,,0,0.827,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/33624,https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_MaskGWM_A_Generalizable_Driving_World_Model_with_Video_Mask_Reconstruction_CVPR_2025_paper.pdf,offline_cvpr,,World models that forecast environmental changes from actions are vital for autonomous driving models with strong generalization. The prevailing driving world model mainly build on pixel-level video prediction model. Although these models can produce high-fidelity video sequences with advanced diffu
120,34434,HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks,Maria Pilligua; Danna Xue; Javier Vazquez-Corral,2025,CVPR 2025,main,Poster,,,0,0.816,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/34434,https://openaccess.thecvf.com/content/CVPR2025/papers/Pilligua_HyperNVD_Accelerating_Neural_Video_Decomposition_via_Hypernetworks_CVPR_2025_paper.pdf,offline_cvpr,,"Decomposing a video into a layer-based representation is crucial for easy video editing for the creative industries, as it enables independent editing of specific layers. Existing video-layer decomposition models rely on implicit neural representations (INRs) trained independently for each video, ma"
121,W49UjcpGxx,FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality,Zhengyao Lv; Chenyang Si; Junhao Song; Zhenyu Yang; Yu Qiao,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",Efficient Video Synthesis,0,0.815,0.000,,https://iclr.cc/virtual/2025/poster/29385,https://openreview.net/pdf?id=W49UjcpGxx,offline_iclr,,"In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades vid"
122,EKGJl7IHRw,ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models,Zixun Fang; Kai Zhu; Zhiheng Liu; Yu Liu; Wei Zhai,2025,NIPS 2025,main,Poster,deep_learning,Diffusion Models,0,0.809,0.000,,https://openreview.net/forum?id=EKGJl7IHRw,,offline_nips,,"Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to the inherent modality gap between panoramic data and perspecti"
123,KZn7TDOL4J,MagCache: Fast Video Generation with Magnitude-Aware Cache,Zehong Ma; Longhui Wei; Feng Wang; Shiliang Zhang; Qi Tian,2025,NIPS 2025,main,Poster,applications,Fast Video Generation;Magnitude-Aware Cache;Training-Free Acceleration,0,0.799,0.000,,https://openreview.net/forum?id=KZn7TDOL4J,,offline_nips,,Existing acceleration techniques for video diffusion models often rely on uniform heuristics or time-embedding variants to skip timesteps and reuse cached features. These approaches typically require extensive calibration with curated prompts and risk inconsistent outputs due to prompt-specific over
124,33447,Improved Video VAE for Latent Video Diffusion Model,Pingyu Wu; Kai Zhu; Yu Liu; Liming Zhao; Wei Zhai,2025,CVPR 2025,main,Poster,,,0,0.797,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/33447,https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Improved_Video_VAE_for_Latent_Video_Diffusion_Model_CVPR_2025_paper.pdf,offline_cvpr,,"Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation models. While most existing video VAEs inflate a pre-trained image VAE into the 3D causal structure for temporal-spatial"
125,gY9yOGYB48,VORTA: Efficient Video Diffusion via Routing Sparse Attention,Wenhao Sun; Rong-Cheng Tu; Yifu Ding; Jingyi Liao; Zhao Jin,2025,NIPS 2025,main,Poster,applications,Video Generation;Diffusion Models;Vision Transformers;Efficient Models,0,0.796,0.000,,https://openreview.net/forum?id=gY9yOGYB48,,offline_nips,,"Video diffusion transformers have achieved remarkable progress in high-quality video generation, but remain computationally expensive due to the quadratic complexity of attention over high-dimensional video sequences.
Recent acceleration methods enhance the efficiency by exploiting the local sparsit"
126,8pusxkLEQO,ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation,Zongyi Li; Shujie HU; Shujie LIU; Long Zhou; Jeongsoo Choi,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",transformer; video generation; diffusion,0,0.789,0.000,,https://iclr.cc/virtual/2025/poster/30742,https://openreview.net/pdf?id=8pusxkLEQO,offline_iclr,,"Text-to-video (T2V) models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant challenge. 
To generate high-quality, dynamic, an"
127,N6SccBt3EF,EVA: An Embodied World Model for Future Video Anticipation,Xiaowei Chi; Hengyuan Zhang; Chun-Kai Fan; Xingqun Qi; Rongyu Zhang,2025,ICLR 2025,main,Reject,"applications to robotics, autonomy, planning",World Model;Video Generation;Visual Language Model;Embodied AI,0,0.776,0.000,,https://openreview.net/forum?id=N6SccBt3EF,,offline_iclr,,"World models integrate raw data from various modalities—such as images and language to simulate comprehensive interactions in the world, thereby displaying crucial roles in fields like mixed reality and robotics. 
Yet, applying the world model for accurate video prediction is quite challenging due t"
128,UlsI4z3QQP,Morse: Fast Sampling for Accelerating Diffusion Models Universally,Chao Li; Jiawei Fan; Anbang Yao,2025,ICLR 2025,main,Reject,generative models,Diffusion models;image generation;text-to-image generation;model acceleration,0,0.773,0.000,,https://openreview.net/forum?id=UlsI4z3QQP,,offline_iclr,,"In this paper, we present Morse, a simple and universal framework for accelerating diffusion models. The key insight of Morse is to reformulate the iterative generation (from noise to data) process via taking advantage of fast jump sampling and adaptive residual feedback strategies. Specifically, Mo"
129,aVyJwS1fqQ,Mani-WM: An Interactive World Model for Real-Robot Manipulation,Fangqi Zhu; Hongtao Wu; Song Guo; Yuxiao Liu; Chilam Cheang,2025,ICLR 2025,main,Withdraw,"applications to robotics, autonomy, planning",World Model;Video Generation;Robot Manipulation,0,0.772,0.000,,https://openreview.net/forum?id=aVyJwS1fqQ,,offline_iclr,,"Scalable robot learning in the real world is limited by the cost and safety issues of real robots. In addition, rolling out robot trajectories in the real world can be time-consuming and labor-intensive. In this paper, we propose to learn an interactive world model for robot manipulation as an alter"
130,hYovE4nHTt,Radial Attention: $\mathcal O(n \log n)$ Sparse Attention for Long Video Generation,Xingyang Li; Muyang Li; Tianle Cai; Haocheng Xi; Shuo Yang,2025,NIPS 2025,main,Poster,deep_learning,video generation;sparse attention;diffusion models,0,0.771,0.000,,https://openreview.net/forum?id=hYovE4nHTt,,offline_nips,,"Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we term Spatiotemporal Ener"
131,DxT3e2f1jc,Video-Infinity: Distributed Long Video Generation,Zhenxiong Tan; Xingyi Yang; Songhua Liu; Xinchao Wang,2025,ICLR 2025,main,Withdraw,generative models,diffusion model;video generation,0,0.746,0.000,,https://openreview.net/forum?id=DxT3e2f1jc,,offline_iclr,,"Diffusion models have recently achieved remarkable results for video generation. Despite the encouraging performances, the generated videos are typically constrained to a small number of frames, resulting in clips lasting merely a few seconds. The primary challenges in producing longer videos includ"
132,35120,Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT Acceleration,Haipeng Fang; Sheng Tang; Juan Cao; Enshuo Zhang; Fan Tang,2025,CVPR 2025,main,Poster,,,0,0.679,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/35120,https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Attend_to_Not_Attended_Structure-then-Detail_Token_Merging_for_Post-training_DiT_CVPR_2025_paper.pdf,offline_cvpr,,"Diffusion transformers have shown exceptional performance in visual generation but incur high computational costs. Token reduction techniques that compress models by sharing the denoising process among similar tokens have been introduced. However, existing approaches neglect the denoising priors of "
133,2xS4VtpApy,FastVID: Dynamic Density Pruning for Fast Video Large Language Models,Leqi Shen; Guoqiang Gong; Tao He; Yifeng Zhang; pengzhang liu,2025,NIPS 2025,main,Poster,deep_learning,Video Large Language Models;Inference Acceleration,0,0.654,0.000,,https://openreview.net/forum?id=2xS4VtpApy,,offline_nips,,"Video Large Language Models have demonstrated strong video understanding capabilities, yet their practical deployment is hindered by substantial inference costs caused by redundant video tokens. 
Existing pruning techniques fail to effectively exploit the spatiotemporal redundancy present in video d"
134,32785,SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models,Jaerin Lee; Daniel Sungho Jung; Kanggeon Lee; Kyoung Mu Lee,2025,CVPR 2025,main,Poster,,,0,0.654,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/32785,https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_SemanticDraw_Towards_Real-Time_Interactive_Content_Creation_from_Image_Diffusion_Models_CVPR_2025_paper.pdf,offline_cvpr,,"We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each encoding prescribed semantic meaning. In order to maximize the productivity of content creators and to fully realize their "
135,DkJImu7t3A,DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution,Zheng Chen; Zichen Zou; Kewei Zhang; Xiongfei Su; Xin Yuan,2025,NIPS 2025,main,Poster,applications,One-Step Diffusion;Real-World;Video Super‑Resolution,0,0.650,0.000,,https://openreview.net/forum?id=DkJImu7t3A,,offline_nips,,"Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely slow. Sampling acceleration techniques, particularly single-step, provide a potential solution. Nonetheless, achieving one"
136,WPU17d1l7R,Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation,Shuo Yang; Haocheng Xi; Yilong Zhao; Muyang Li; Jintao Zhang,2025,NIPS 2025,main,Spotlight,deep_learning,video diffusion;attention acceleration,0,0.621,0.000,,https://openreview.net/forum?id=WPU17d1l7R,,offline_nips,,"Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. 
By computing only critical tokens, sparse attention reduces computational costs and offers a promising acceleration approach. 
However, we identify that"
137,33437,RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories,Huiyang Shao; Xin Xia; Yuhong Yang; Yuxi Ren; Xing Wang,2025,CVPR 2025,main,Poster,,,0,0.620,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/33437,https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_RayFlow_Instance-Aware_Diffusion_Acceleration_via_Adaptive_Flow_Trajectories_CVPR_2025_paper.pdf,offline_cvpr,,"Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while aiming to reduce steps, often compromise sample quality, controllability, or introduce training complexities. Therefore, w"
138,JbPb6RieNC,Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge,Haomiao Xiong; Zongxin Yang; Jiazuo Yu; Yunzhi Zhuge; Lu Zhang,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",Streaming Video Understanding; Video MLLM; Hierarchical Memory System,0,0.620,0.000,,https://iclr.cc/virtual/2025/poster/30097,https://openreview.net/pdf?id=JbPb6RieNC,offline_iclr,,"Recent advances in Large Language Models (LLMs) have enabled the development of Video-LLMs, advancing multimodal learning by bridging video data with language tasks. However, current video understanding models struggle with processing long video sequences, supporting multi-turn dialogues, and adapti"
139,IIGiVRKJYa,ASDSV: Multimodal Generation Made Efficient with Approximate Speculative Diffusion and Speculative Verification,Kaijun Zhou; Xingyu Yan; Xingda Wei; Xijun Li; Jinyu Gu,2025,NIPS 2025,main,Poster,infrastructure,Speculative Diffusion;Diffusion model;Multimodel Generation;Inference acceleration,0,0.614,0.000,,https://openreview.net/forum?id=IIGiVRKJYa,,offline_nips,,"Diffusion in transformer is central to advances in high-quality multimodal generation 
but suffer from high inference latency due to their iterative nature. 
Inspired by speculative decoding's success in accelerating large language models, 
we propose Approximate Speculative Diffusion with Speculati"
140,hDBrQ4DApF,Real-Time Video Generation with Pyramid Attention Broadcast,Xuanlei Zhao; Xiaolong Jin; Kai Wang; Yang You,2025,ICLR 2025,main,Poster,generative models,Diffusion Acceleration;DiT;Video Generation;Efficient;Real-Time;Parallelism;Sequence Parallelism,0,0.609,0.000,,https://iclr.cc/virtual/2025/poster/28773,https://openreview.net/pdf?id=hDBrQ4DApF,offline_iclr,,"We present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern, indicating significant redundancy. We mitigate t"
141,nNYA7tcJSE,ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler,Serin Yang; Taesung Kwon; Jong Chul Ye,2025,ICLR 2025,main,Poster,generative models,Keyframe interpolation;Stable video diffusion;Bidirectional diffusion sampling,0,0.600,0.000,,https://iclr.cc/virtual/2025/poster/28410,https://openreview.net/pdf?id=nNYA7tcJSE,offline_iclr,,"Recent progress in large-scale text-to-video (T2V) and image-to-video (I2V) diffusion models has greatly enhanced video generation, especially in terms of keyframe interpolation. However, current image-to-video diffusion models, while powerful in generating videos from a single conditioning frame, n"
142,Jt1gGIumJo,Highlight Diffusion: Training-Free Attention Guided Acceleration for Text-to-Image Models,Kyuseok Nam; Yulhwa Kim; Jeongwoo Park,2025,ICLR 2025,main,Withdraw,generative models,Diffusion model;Cross attention;Acceleration;Text to Image,0,0.596,0.000,,https://openreview.net/forum?id=Jt1gGIumJo,,offline_iclr,,"Diffusion models have achieved exceptional results in image synthesis, yet their sequential processing nature imposes significant computational demands and latency, posing challenges for practical deployment. In this paper, we present Highlight Diffusion: a training-free novel acceleration approach "
143,0n4bS0R5MM,VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control,Sherwin Bahmani; Ivan Skorokhodov; Aliaksandr Siarohin; Willi Menapace; Guocheng Qian,2025,ICLR 2025,main,Poster,generative models,video generation;3d;diffusion,0,0.594,0.000,,https://iclr.cc/virtual/2025/poster/32114,https://openreview.net/pdf?id=0n4bS0R5MM,offline_iclr,,"Modern text-to-video synthesis models demonstrate coherent, photorealistic generation of complex videos from a text description. However, most existing models lack fine-grained control over camera movement, which is critical for downstream applications related to content creation, visual effects, an"
144,YA1Ur2eGFl,Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models,Zhening Xing; Gereon Fox; Yanhong Zeng; Xingang Pan; Mohamed Elgharib,2025,ICLR 2025,main,Withdraw,generative models,Generative Model;Video Generation;Video Translation,0,0.587,0.000,,https://openreview.net/forum?id=YA1Ur2eGFl,,offline_iclr,,"Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which models correlations between the current token and *previous* tokens.
However, video streaming remains much less explored, de"
145,34521,Exploiting Temporal State Space Sharing for Video Semantic Segmentation,Syed Ariff Syed Hesham; Yun Liu; Guolei Sun; Henghui Ding; Jing Yang,2025,CVPR 2025,main,Poster,,,0,0.585,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/34521,https://openaccess.thecvf.com/content/CVPR2025/papers/Hesham_Exploiting_Temporal_State_Space_Sharing_for_Video_Semantic_Segmentation_CVPR_2025_paper.pdf,offline_cvpr,,"Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes. Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements. To this end, we i"
146,ICR3swcnaa,Spatio-temporal Diffusion Transformer for Action Recognition,Jing Gu; Yusong Bai; Desheng Zhai; Biao Hou; Shasha Mao,2025,ICLR 2025,main,Reject,"other topics in machine learning (i.e., none of the above)",Video action recognition;fine-grained action;information diffusion;spatiotemporal feature,0,0.575,0.000,,https://openreview.net/forum?id=ICR3swcnaa,,offline_iclr,,"Video action recognition has aroused the research interest of many scholars, and has been widely used in public surveillance, video review, sports events and other fields. However, the high similarity of video background and the long time span of video action bring serious challenges to action recog"
147,32779,WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model,Zongjian Li; Bin Lin; Yang Ye; Liuhan Chen; Xinhua Cheng,2025,CVPR 2025,main,Poster,,,0,0.573,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/32779,https://openaccess.thecvf.com/content/CVPR2025/papers/Li_WF-VAE_Enhancing_Video_VAE_by_Wavelet-Driven_Energy_Flow_for_Latent_CVPR_2025_paper.pdf,offline_cvpr,,"Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes"
148,6Gb7VfTKY7,Parallel simulation for sampling under isoperimetry and score-based diffusion models,Huanjian Zhou; Masashi Sugiyama,2025,ICLR 2025,main,Reject,"probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)",parallel sampling;log-concave sampling;diffusion model;score-based generative modeling;ddpm,0,0.570,0.000,,https://openreview.net/forum?id=6Gb7VfTKY7,,offline_iclr,,"In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value prob"
149,2JihLwirxO,ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models,Jianrong Lu; Zhiyu Zhu; Junhui Hou,2025,ICLR 2025,main,Poster,generative models,Diffusion Models;,0,0.567,0.000,,https://iclr.cc/virtual/2025/poster/31147,https://openreview.net/pdf?id=2JihLwirxO,offline_iclr,,"This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic systems perspective, in which the inherent sequential nature is transformed into a parallel sampling process. Specifically, we pro"
150,E1N1oxd63b,ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation,Tianchen Zhao; Tongcheng Fang; Haofeng Huang; Rui Wan; Widyadewi Soedarmadji,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",video generation;low-bit quantization;diffusion model,0,0.565,0.000,,https://iclr.cc/virtual/2025/poster/30429,https://openreview.net/pdf?id=E1N1oxd63b,offline_iclr,,"Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generation lead to increased computational and memory costs, posin"
151,34408,Mimir: Improving Video Diffusion Models for Precise Text Understanding,Shuai Tan; Biao Gong; Yutong Feng; Kecheng Zheng; Dandan Zheng,2025,CVPR 2025,main,Poster,,,0,0.565,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/34408,https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_Mimir_Improving_Video_Diffusion_Models_for_Precise_Text_Understanding_CVPR_2025_paper.pdf,offline_cvpr,,"Text serves as the key control signal in video generation due to its narrative nature. To render text descriptions into video clips, current video diffusion models borrow features from text encoders yet struggle with limited text comprehension. The recent success of large language models (LLMs) show"
152,jqDtzUQkmu,Accelerating Parallel Diffusion Model Serving with Residual Compression,Jiajun Luo; Yicheng Xiao; Jianru Xu; Yangxiu You; Rongwei Lu,2025,NIPS 2025,main,Poster,infrastructure,diffusion model acceleration;parallel inference,0,0.548,0.000,,https://openreview.net/forum?id=jqDtzUQkmu,,offline_nips,,"Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However, parallel inference introduces significant communication overhead from exchanging large activations between devices, lim"
153,5xwyxupsLL,PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference,Jiarui Fang; Jinzhe Pan; Aoyu Li; Xibo Sun; WANG Jiannan,2025,NIPS 2025,main,Poster,infrastructure,Diffusion Model;GPU;Sequence Parallel;Pipeline Parallel,0,0.541,0.000,,https://openreview.net/forum?id=5xwyxupsLL,,offline_nips,,"This paper presents PipeFusion, an innovative parallel methodology to tackle the high latency issues associated with generating high-resolution images using diffusion transformers (DiTs) models.  PipeFusion partitions images into patches and the model layers across multiple GPUs. It employs a patch-"
154,bjkQTInGes,Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion,Hao Wen; Zehuan Huang; Yaohui Wang; Xinyuan Chen; Lu Sheng,2025,ICLR 2025,main,Withdraw,generative models,3D; Video Diffusion Model; 3D generation,0,0.534,0.000,,https://openreview.net/forum?id=bjkQTInGes,,offline_iclr,,"Existing image-to-3D creation methods typically split the task into multi-view image generation and 3D reconstruction, leading to two main limitations: (1) multi-view bias, where geometric inconsistencies arise because multi-view diffusion models ensure image-level rather than 3D consistency; (2) mi"
155,,Virtually Being: Customizing Camera-Controllable Video Diffusion Models with Volumetric Performance Captures,Yuancheng Xu; Wenqi Xian; Li Ma; Julien Philip; A. Taşel,2025,Proceedings of the SIGGRAPH Asia 2025 Conference Papers,,,,,0,0.000,0.000,10.1145/3757377.3763888,https://www.semanticscholar.org/paper/b123d424f5d3d2889b08671bb8e07701b023c2e2,,semantic_scholar,,We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train the character consistency component with recorded volumetric capture performances re-rendered with diverse camera trajecto
156,,Virtually Being: Customizing Camera-Controllable Video Diffusion Models with Multi-View Performance Captures,Yuancheng Xu; Wenqi Xian; Li Ma; Julien Philip; A. Taşel,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2510.14179,https://www.semanticscholar.org/paper/021631d09c28b8c5996622bad946ae97ce345ba6,,semantic_scholar,,We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train the character consistency component with recorded volumetric capture performances re-rendered with diverse camera trajecto
157,,AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path,Zhengyang Yu; Akio Hayakawa; Masato Ishii; Qingtao Yu; Takashi Shibuya,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/e5c61a476e570bad1f0303ef1c57c559db3a7ae0,,semantic_scholar,,"Autoregressive video diffusion models (AR-VDMs) show strong promise as scalable alternatives to bidirectional VDMs, enabling real-time and interactive applications. Yet there remains room for improvement in their sample fidelity. A promising solution is inference-time alignment, which optimizes the "
158,,SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time,Zhening Huang; Hyeonho Jeong; Xuelin Chen; Yulia Gryaditskaya; Tuanfeng Y. Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25075v1,https://arxiv.org/pdf/2512.25075v1,arxiv,,"We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous"
159,,Randomization Times under Quantum Chaotic Hamiltonian Evolution,Souradeep Ghosh; Nicholas Hunter-Jones; Joaquin F. Rodriguez-Nieva,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25074v1,https://arxiv.org/pdf/2512.25074v1,arxiv,,"Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics suc"
160,,GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction,Yi-Chuan Huang; Hao-Jen Chien; Chin-Yang Lin; Ying-Huan Chen; Yu-Lun Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25073v1,https://arxiv.org/pdf/2512.25073v1,arxiv,,"Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to"
161,,Edit3r: Instant 3D Scene Editing from Sparse Unposed Images,Jiageng Liu; Weijie Lyu; Xueting Li; Yejie Guo; Ming-Hsuan Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25071v1,https://arxiv.org/pdf/2512.25071v1,arxiv,,"We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealisti"
162,,Coordinated Humanoid Manipulation with Choice Policies,Haozhi Qi; Yen-Jen Wang; Toru Lin; Brent Yi; Yi Ma,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25072v1,https://arxiv.org/pdf/2512.25072v1,arxiv,,"Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address t"
163,,Scaling Open-Ended Reasoning to Predict the Future,Nikhil Chandak; Shashwat Goel; Ameya Prabhu; Moritz Hardt; Jonas Geiping,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25070v1,https://arxiv.org/pdf/2512.25070v1,arxiv,,"High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a f"
164,,From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing,Xu He; Haoxian Zhang; Hejia Chen; Changyuan Zheng; Liyang Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25066v1,https://arxiv.org/pdf/2512.25066v1,arxiv,,"Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this wit"
165,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25065v1,https://arxiv.org/pdf/2512.25065v1,arxiv,,"Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously g"
166,,Feeling Blue: Constructing a Robust SALT3 UV Template and Constraining its Redshift Dependency,Qinan Wang; David O. Jones; Justin D. R. Pierel; Matthew R. Siebert; W. D'Arcy Kenworthy,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25064v1,https://arxiv.org/pdf/2512.25064v1,arxiv,,"Upcoming cosmological surveys will obtain numerous rest-frame ultraviolet (UV) observations of Type Ia supernovae (SNe Ia), yet there is concern about how standardizable SNe Ia are in the UV. In this work, we train a robust optical--UV SED model for SNe Ia (SALT3-UV) with the open-source model-train"
167,,Many Minds from One Model: Bayesian Transformers for Population Intelligence,Diji Yang; Yi Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25063v1,https://arxiv.org/pdf/2512.25063v1,arxiv,,"Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we prop"
168,,Melting curve of correlated iron at Earth's core conditions from machine-learned DFT+DMFT,Rishi Rao; Li Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25061v1,https://arxiv.org/pdf/2512.25061v1,arxiv,,"Reliable constraints on iron's melting curve at Earth's inner-core boundary require accurate finite-temperature electronic correlations, yet DFT+DMFT calculations remain too costly for large-scale thermodynamic sampling. Here, we develop a machine-learning accelerator for charge self-consistent DFT+"
169,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25059v1,https://arxiv.org/pdf/2512.25059v1,arxiv,,"Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training"
170,,Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework,Liliang Wang; Alex Gorodetsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25056v1,https://arxiv.org/pdf/2512.25056v1,arxiv,,"Online joint estimation of unknown parameters and states in a dynamical system with uncertainty quantification is crucial in many applications. For example, digital twins dynamically update their knowledge of model parameters and states to support prediction and decision-making. Reliability and comp"
171,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25055v1,https://arxiv.org/pdf/2512.25055v1,arxiv,,This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three 
172,,Fluid dynamics as intersection problem,Nikita Nekrasov; Paul Wiegmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25053v1,https://arxiv.org/pdf/2512.25053v1,arxiv,,"We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, a"
173,,The PDE-ODI principle and cylindrical mean curvature flows,Richard H. Bamler; Yi Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25050v1,https://arxiv.org/pdf/2512.25050v1,arxiv,,"We introduce a new approach for analyzing ancient solutions and singularities of mean curvature flow that are locally modeled on a cylinder. Its key ingredient is a general mechanism, called the \emph{PDE--ODI principle}, which converts a broad class of parabolic differential equations into systems "
174,,All optical Lithography for Spatiotemporal Patterning of Azopolymer Microreliefs,I Komang Januariyasa; Francesco Reda; Nikolai Liubimtsev; Marina Saphiannikova; Fabio Borbone,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25048v1,https://arxiv.org/pdf/2512.25048v1,arxiv,,"Microstructured surfaces are central to photonics, biointerfaces, and functional coatings, yet they are typically fabricated through multi-step lithographic workflows requiring masks or molds and post-processing. Azopolymers provide an alternative route by converting structured optical fields into s"
175,,Extreme nonlinear optics in optical fibers,Mario Ferraro; Bertrand Kibler; Pierre Béjot; Frédéric Gérome; Benoit Debord,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25046v1,https://arxiv.org/pdf/2512.25046v1,arxiv,,"This paper reviews the field of extreme nonlinear optics in optical fibers, highlighting key phenomena and advancements. It discusses multiple ionization effects caused by femtosecond laser pulses that generate plasma and induce permanent material modifications, as well as plasma luminescence and it"
176,,Bayesian Elastic Net Regression with Structured Prior Dependence,Christopher M. Hans; Ningyi Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25045v1,https://arxiv.org/pdf/2512.25045v1,arxiv,,"Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for standard Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, incorporating depende"
177,,Thin Tree Verification is coNP-Complete,Alice Moayyedi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25043v1,https://arxiv.org/pdf/2512.25043v1,arxiv,,"An $α$-thin tree $T$ of a graph $G$ is a spanning tree such that every cut of $G$ has at most an $α$ proportion of its edges in $T$. The Thin Tree Conjecture proposes that there exists a function $f$ such that for any $α> 0$, every $f(α)$-edge-connected graph has an $α$-thin tree. Aside from its ind"
178,,Compound Estimation for Binomials,Yan Chen; Lihua Lei,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25042v1,https://arxiv.org/pdf/2512.25042v1,arxiv,,"Many applications involve estimating the mean of multiple binomial outcomes as a common problem -- assessing intergenerational mobility of census tracts, estimating prevalence of infectious diseases across countries, and measuring click-through rates for different demographic groups. The most standa"
179,,Towards precision cosmology with Voids x CMB correlations (I): Roman-Agora mock catalogs and pipeline validation,Mar Pérez Sar; Carlos Hernández Monteagudo; András Kovács; Alice Pisani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25040v1,https://arxiv.org/pdf/2512.25040v1,arxiv,,"We construct and validate a set of multi-purpose mock galaxy catalogs designed to capture, to different degrees of accuracy, the main characteristics of the Nancy Grace Roman Space Telescope survey. These catalogs provide a foundation for void statistics and various CMB cross-correlation analyses. O"
180,,Anomalous (3+1)d Fermionic Topological Quantum Field Theories via Symmetry Extension,Zheyan Wan; Juven Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25038v1,https://arxiv.org/pdf/2512.25038v1,arxiv,,"Discrete finite-group global symmetries may suffer from nonperturbative 't-Hooft anomalies. Such global anomalies can be canceled by anomalous symmetry-preserving topological quantum field theories (TQFTs), which contain no local point operators but only extended excitations such as line and surface"
181,,Large Neutrino-Dark Matter Interactions: From Effective Field Theory to Ultraviolet Completions,K. S. Babu; P. S. Bhupal Dev; Anil Thapa,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25035v1,https://arxiv.org/pdf/2512.25035v1,arxiv,,"We develop a general effective field theory (EFT) framework for neutrino-dark matter (DM) interactions, and apply it to systematically find all possible gauge-invariant ultraviolet (UV) completions at a given EFT operator dimension. Our goal here is to find simple UV-complete models that can realize"
182,,Generative Classifiers Avoid Shortcut Solutions,Alexander C. Li; Ananya Kumar; Deepak Pathak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25034v1,https://arxiv.org/pdf/2512.25034v1,arxiv,,"Discriminative approaches to classification often learn shortcuts that hold in-distribution but fail even under minor distribution shift. This failure mode stems from an overreliance on features that are spuriously correlated with the label. We show that generative classifiers, which use class-condi"
183,,AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG,Chao Peng; Bin Wang; Zhilei Long; Jinfang Sheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25052v1,https://arxiv.org/pdf/2512.25052v1,arxiv,,"Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for"
184,,Universal polar dual pairs of spherical codes found in $E_8$ and $Λ_{24}$,S. V. Borodachov; P. G. Boyvalenkov; P. D. Dragnev; D. P. Hardin; E. B. Saff,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25037v1,https://arxiv.org/pdf/2512.25037v1,arxiv,,"We identify universal polar dual pairs of spherical codes $C$ and $D$ such that for a large class of potential functions $h$ the minima of the discrete $h$-potential of $C$ on the sphere occur at the points of $D$ and vice versa. Moreover, the minimal values of their normalized potentials are equal."
185,,EF(X) Orientations: A Parameterized Complexity Perspective,Sotiris Kanellopoulos; Edouard Nemery; Christos Pergaminelis; Minas Marios Sotiriou; Manolis Vasilakis,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25033v1,https://arxiv.org/pdf/2512.25033v1,arxiv,,"The concept of fair orientations in graphs was introduced by Christodoulou, Fiat, Koutsoupias, and Sgouritsa in 2023, naturally modeling fair division scenarios in which resources are only contested by neighbors. In this model, vertices represent agents and undirected edges represent goods; edges ha"
186,,"A Review on Background, Technology, Comparison, and Future Tendency of Video Generation",Zhiyu Han,2025,Highlights in Science Engineering and Technology,,,,,0,0.000,0.000,10.54097/18313836,https://www.semanticscholar.org/paper/135eb9744908d6d04bdb7f73f7cb217c88d8d2cc,,semantic_scholar,,"Video generation techniques incorporate recent advances in deep learning and generative modeling, and are widely used in film and television, education, advertising, virtual reality, and other fields. The background lies in the growing need to generate high-resolution, dynamically consistent, and se"
187,NadTwTODgC,Diffusion for World Modeling: Visual Details Matter in Atari,Eloi Alonso; Adam Jelley; Vincent Micheli; Anssi Kanervisto; Amos Storkey,2024,NIPS 2024,main,Spotlight,reinforcement_learning,World models;diffusion models;reinforcement learning;generative models;Atari,0,0.811,0.000,,https://neurips.cc/virtual/2024/poster/95428,https://openreview.net/pdf?id=NadTwTODgC,offline_nips,,"World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model environment dynamics. However, this compression into a compact discrete represen"
188,bAXmvOLtjA,Diffusion World Models,Eloi Alonso; Adam Jelley; Anssi Kanervisto; Tim Pearce,2024,ICLR 2024,main,Reject,reinforcement learning,World models;diffusion models;reinforcement learning;generative modeling,0,0.809,0.000,,https://openreview.net/forum?id=bAXmvOLtjA,,offline_iclr,,"World models constitute a powerful and versatile tool for decision-making. Through their ability to predict future states of the world, they can replace environments for safe and fast simulation, and/or be leveraged for search at decision time. Advances in generative modeling have led to the develop"
189,85Af6AcMo5,SciRE-Solver: Accelerating Diffusion Models Sampling by Score-integrand Solver with Recursive Difference,Shigui Li; Wei Chen; Delu Zeng,2024,ICLR 2024,main,Reject,generative models,Diffusion Models;Sampler;Accelerating,0,0.792,0.000,,https://openreview.net/forum?id=85Af6AcMo5,,offline_iclr,,"One downside of Diffusion models (DMs) is their slow iterative process. Recent algorithms for fast sampling are designed from the 
differential equations. However, in the fast algorithms, estimating the derivative of the score function evaluations becomes intractable due to the complexity of large-s"
190,NsqxN9iOJ7,Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation,Yuanhao Zhai; Kevin Lin; Zhengyuan Yang; Linjie Li; Jianfeng Wang,2024,NIPS 2024,main,Poster,diffusion_based_models,consistency distillation;video diffusion models;diffusion distillation;text-to-video generation,0,0.786,0.000,,https://neurips.cc/virtual/2024/poster/95405,https://openreview.net/pdf?id=NsqxN9iOJ7,offline_nips,,"Image diffusion distillation achieves high-fidelity generation with very few sampling steps. However, directly applying these techniques to video models results in unsatisfied frame quality. This issue arises from the limited frame appearance quality in public video datasets, affecting the performan"
191,Psl75UCoZM,Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion,Lunjun Zhang; Yuwen Xiong; Ze Yang; Sergio Casas; Rui Hu,2024,ICLR 2024,main,Poster,"applications to robotics, autonomy, planning",discrete diffusion; world model; autonomous driving,0,0.777,0.000,,https://iclr.cc/virtual/2024/poster/18691,https://openreview.net/pdf?id=Psl75UCoZM,offline_iclr,,"Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling world models on robotic applications such as autonomous driving has been somewhat less rapid than scaling language models "
192,F9NDzHQtOl,Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity,Haoxuan Chen; Yinuo Ren; Lexing Ying; Grant M. Rotskoff,2024,NIPS 2024,main,Spotlight,diffusion_based_models,diffusion model;parallel sampling;stochastic differential equations;probability flow ode,0,0.775,0.000,,https://neurips.cc/virtual/2024/poster/95999,https://openreview.net/pdf?id=F9NDzHQtOl,offline_nips,,"Diffusion models have become a leading method for generative modeling of both image and scientific data.
As these models are costly to train and \emph{evaluate}, reducing the inference cost for diffusion models remains a major goal.
Inspired by the recent empirical success in accelerating diffusion "
193,YbhHz0X2j5,VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation,Youpeng Wen; Junfan Lin; Yi Zhu; Jianhua Han; Hang Xu,2024,NIPS 2024,main,Poster,robotics,Imitation learning;Video prediction;Robot Manipulation,0,0.774,0.000,,https://neurips.cc/virtual/2024/poster/94687,https://openreview.net/pdf?id=YbhHz0X2j5,offline_nips,,"Recent advancements utilizing large-scale video data for learning video generation models demonstrate significant potential in understanding complex physical dynamics. It suggests the feasibility of leveraging diverse robot trajectory data to develop a unified, dynamics-aware model to enhance robot "
194,46jtDC6gXu,AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising,Zigeng Chen; Xinyin Ma; Gongfan Fang; Zhenxiong Tan; Xinchao Wang,2024,NIPS 2024,main,Poster,diffusion_based_models,Diffusion Model;Inference Acceleration,0,0.683,0.000,,https://neurips.cc/virtual/2024/poster/96695,https://openreview.net/pdf?id=46jtDC6gXu,offline_nips,,"Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-denoising nature gives rise to high cumulative latency, thereby precluding the possibilities of parallel computation. To"
195,29424,IQ-VFI: Implicit Quadratic Motion Estimation for Video Frame Interpolation,Mengshun Hu; Kui Jiang; Zhihang Zhong; Zheng Wang; Yinqiang Zheng,2024,CVPR 2024,main,Poster,,,0,0.609,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29424,https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_IQ-VFI_Implicit_Quadratic_Motion_Estimation_for_Video_Frame_Interpolation_CVPR_2024_paper.pdf,offline_cvpr,,Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames.
196,LH94zPv8cu,Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models,Giannis Daras; Weili Nie; Karsten Kreis; Alex Dimakis; Morteza Mardani,2024,NIPS 2024,main,Poster,diffusion_based_models,noise warping;inverse problems;diffusion models;equivariance,0,0.592,0.000,,https://neurips.cc/virtual/2024/poster/95607,https://openreview.net/pdf?id=LH94zPv8cu,offline_nips,,"Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these problems, in this paper, we view frames as continuous functions in the 2D space, and videos as a sequence of continuous warpi"
197,29750,360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model,Qian Wang; Weiqi Li; Chong Mou; Xinhua Cheng; Jian Zhang,2024,CVPR 2024,main,Poster,,,0,0.580,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29750,https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_360DVD_Controllable_Panorama_Video_Generation_with_360-Degree_Video_Diffusion_Model_CVPR_2024_paper.pdf,offline_cvpr,,Panorama video recently attracts more interest in both study and application courtesy of its immersive experience. Due to the expensive cost of capturing 360-degree panoramic videos generating desirable panorama videos by prompts is urgently required. Lately the emerging text-to-video (T2V) diffusio
198,29381,Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner,Mengfei Xia; Yujun Shen; Changsong Lei; Yu Zhou; Deli Zhao,2024,CVPR 2024,main,Poster,,,0,0.579,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29381,https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_Towards_More_Accurate_Diffusion_Model_Acceleration_with_A_Timestep_Tuner_CVPR_2024_paper.pdf,offline_cvpr,,A diffusion model which is formulated to produce an image using thousands of denoising steps usually suffers from a slow inference speed. Existing acceleration algorithms simplify the sampling by skipping most steps yet exhibit considerable performance degradation. By viewing the generation of diffu
199,eeaKRQIaYd,Unsupervised Sign Language Translation and Generation,Zhengsheng Guo; Zhiwei He; Wenxiang Jiao; Xing Wang; Rui Wang,2024,ICLR 2024,main,Reject,"representation learning for computer vision, audio, language, and other modalities",unsupervised;sign language translation;natural language processing,0,0.572,0.000,,https://openreview.net/forum?id=eeaKRQIaYd,,offline_iclr,,"Sign language translation and generation are crucial in facilitating communication between the deaf and hearing communities.However, the scarcity of parallel sign language video-to-text data poses a considerable challenge to developing effective sign language translation and generation systems.Motiv"
200,RPM7STrnVz,VideoTetris: Towards Compositional Text-to-Video Generation,Ye Tian; Ling Yang; Haotian Yang; Yuan Gao; Yufan Deng,2024,NIPS 2024,main,Poster,generative_models,Text-to-Video Generation;Video Diffusion Models,0,0.557,0.000,,https://neurips.cc/virtual/2024/poster/95173,https://openreview.net/pdf?id=RPM7STrnVz,offline_nips,,"Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in object numbers. To address these limitations, we propose Video"
201,31516,VidToMe: Video Token Merging for Zero-Shot Video Editing,Xirui Li; Chao Ma; Xiaokang Yang; Ming-Hsuan Yang,2024,CVPR 2024,main,Poster,,,0,0.551,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/31516,https://openaccess.thecvf.com/content/CVPR2024/papers/Li_VidToMe_Video_Token_Merging_for_Zero-Shot_Video_Editing_CVPR_2024_paper.pdf,offline_cvpr,,Diffusion models have made significant advances in generating high-quality images but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source
202,qCyhvr0GG8,VONet: Unsupervised Video Object Learning With Parallel U-Net Attention and Object-wise Sequential VAE,Haonan Yu; Wei Xu,2024,ICLR 2024,main,Poster,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Unsupervised learning; Video object learning,0,0.547,0.000,,https://iclr.cc/virtual/2024/poster/17743,https://openreview.net/pdf?id=qCyhvr0GG8,offline_iclr,,"Unsupervised video object learning seeks to decompose video scenes into structural object representations without any supervision from depth, optical flow, or segmentation. We present VONet, an innovative approach that is inspired by MONet. While utilizing a U-Net architecture, VONet employs an effi"
203,bzuQtVDxv0,Splatter a Video: Video Gaussian Representation for Versatile Processing,Yang-Tian Sun; Yi-Hua Huang; Lin Ma; Xiaoyang Lyu; Yan-Pei Cao,2024,NIPS 2024,main,Poster,machine_vision,Video Representation; Video Processing,0,0.533,0.000,,https://neurips.cc/virtual/2024/poster/94450,https://openreview.net/pdf?id=bzuQtVDxv0,offline_nips,,"Video representation is a long-standing problem that is crucial for various downstream tasks, such as tracking, depth prediction, segmentation, view synthesis, and editing. However, current methods either struggle to model complex motions due to the absence of 3D structure or rely on implicit 3D rep"
204,F0KTk2plQzO,Accelerating Guided Diffusion Sampling with Splitting Numerical Methods,Suttisak Wizadwongsa; Supasorn Suwajanakorn,2023,ICLR 2023,main,Poster,,Splitting Numerical Methods;Guided Diffusion Models,0,0.773,0.000,,https://iclr.cc/virtual/2023/poster/11604,https://openreview.net/pdf?id=F0KTk2plQzO,offline_iclr,We accelerate guided diffusion sampling using splitting numerical methods.,"Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task. However, one drawback of diffusion models, whether they are guided or unguided, is their slow sampling process. 
Recent techniques can accelerate u"
205,21724,Parallel Diffusion Models of Operator and Image for Blind Inverse Problems,Hyungjin Chung; Jeongsol Kim; Sehui Kim; Jong Chul Ye,2023,CVPR 2023,main,Poster,,,0,0.554,0.000,,https://cvpr.thecvf.com/virtual/2023/poster/21724,https://openaccess.thecvf.com/content/CVPR2023/papers/Chung_Parallel_Diffusion_Models_of_Operator_and_Image_for_Blind_Inverse_CVPR_2023_paper.pdf,offline_cvpr,,"Diffusion model-based inverse problem solvers have demonstrated state-of-the-art performance in cases where the forward operator is known (i.e. non-blind). However, the applicability of the method to blind inverse problems has yet to be explored. In this work, we show that we can indeed solve a fami"
206,23148,DiffCollage: Parallel Generation of Large Content With Diffusion Models,Qinsheng Zhang; Jiaming Song; Xun Huang; Yongxin Chen; Ming-Yu Liu,2023,CVPR 2023,main,Poster,,,0,0.553,0.000,,https://cvpr.thecvf.com/virtual/2023/poster/23148,https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_DiffCollage_Parallel_Generation_of_Large_Content_With_Diffusion_Models_CVPR_2023_paper.pdf,offline_cvpr,,"We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approach is based on a factor graph representation where each factor node represents a portion of the content and a variable n"
207,,Accelerating Video Object Segmentation With Compressed Video,Kai Xu; Angela Yao,2022,CVPR 2022,main,Poster,,,0,0.790,0.000,,,https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.pdf,offline_cvpr,,"We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose a motion vector-based warping method for propagating segmentation masks from ke"
208,,Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems Through Stochastic Contraction,Hyungjin Chung; Byeongsu Sim; Jong Chul Ye,2022,CVPR 2022,main,Poster,,,0,0.770,0.000,,,https://openaccess.thecvf.com/content/CVPR2022/papers/Chung_Come-Closer-Diffuse-Faster_Accelerating_Conditional_Diffusion_Models_for_Inverse_Problems_Through_Stochastic_CVPR_2022_paper.pdf,offline_cvpr,,"Diffusion models have recently attained significant interest within the community owing to their strong performance as generative models. Furthermore, its application to inverse problems have demonstrated state-of-the-art performance. Unfortunately, diffusion models have a critical downside - they a"
209,SGd00Q1lRrw,Multiaffine representations mediate tradeoff between generalization and parallel processing capacity in networks trained to multitask,Gregory Henselman-Petrusek; Tyler Giallanza; Sebastian Musslick; Jonathan Cohen,2021,ICLR 2021,main,Withdraw,,Multitask learning;shared representations;generalization,0,0.540,0.000,,https://openreview.net/forum?id=SGd00Q1lRrw,,offline_iclr,,"Artificial systems currently outperform humans in diverse computational domains, but none has  achieved parity in speed and overall versatility of mastering novel tasks.  A critical component to human success, in this regard, is the ability to redeploy and redirect  data passed between cognitive sub"
210,9EsrXMzlFQY,Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors,Yu Sun; Jiaming Liu; Yiran Sun; Brendt Wohlberg; Ulugbek Kamilov,2021,ICLR 2021,main,Spotlight,,Regularization by denoising;Computational imaging;asynchronous parallel algorithm;Deep denoising priors,0,0.532,0.000,,https://iclr.cc/virtual/2021/poster/3077,https://openreview.net/pdf?id=9EsrXMzlFQY,offline_iclr,,"Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for"
211,14496,Quadratic Video Interpolation,Xiangyu Xu; Li Siyao; Wenxiu Sun; Qian Yin; Ming-Hsuan Yang,2019,NIPS 2019,main,Spotlight,,,0,0.612,0.000,,https://nips.cc/virtual/2019/poster/14496,https://papers.nips.cc/paper_files/paper/2019/file/d045c59a90d7587d8d671b5f5aec4e7c-Paper.pdf,offline_nips,,"Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the com"
212,8b73b1670c,Jerk-Aware Video Acceleration Magnification,Shoichiro Takeda; Kazuki Okami; Dan Mikami; Megumi Isogai; Hideaki Kimata,2018,CVPR 2018,main,Poster,,,0,0.626,0.000,,https://openaccess.thecvf.com/content_cvpr_2018/html/Takeda_Jerk-Aware_Video_Acceleration_CVPR_2018_paper.html,https://openaccess.thecvf.com/content_cvpr_2018/papers/Takeda_Jerk-Aware_Video_Acceleration_CVPR_2018_paper.pdf,offline_cvpr,,"Video magnification reveals subtle changes invisible to the naked eye, but such tiny yet meaningful changes are often hidden under large motions: small deformation of the muscles in doing sports, or tiny vibrations of strings in ukulele playing. For magnifying subtle changes under large motions, vid"
213,,Video Acceleration Magnification,Yichao Zhang; Silvia L. Pintea; Jan C. van Gemert,2017,CVPR 2017,main,Poster,,,0,0.597,0.000,,,https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Video_Acceleration_Magnification_CVPR_2017_paper.pdf,offline_cvpr,,"The ability to amplify or reduce subtle image changes over time is useful in contexts such as video editing, medical video analysis, product quality control and sports. In these contexts there is often large motion present which severely distorts current video amplification methods that magnify chan"
214,4781,Stochastic Proximal Gradient Descent with Acceleration Techniques,Atsushi Nitanda,2014,NIPS 2014,main,Poster,,,0,0.606,0.000,,https://nips.cc/virtual/2014/poster/4781,https://papers.nips.cc/paper_files/paper/2014/file/2d6cd90d4f3fa50e6d9bdbc81a2e3712-Paper.pdf,offline_nips,,"Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This met"
215,d48891e41a,Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units,Feng Yan; Ningyi Xu; Yuan Qi,2009,NIPS 2009,main,Poster,,,0,0.565,0.000,,https://papers.nips.cc/paper_files/paper/2009/hash/ed265bc903a5a097f61d3ec064d96d2e-Abstract.html,https://papers.nips.cc/paper_files/paper/2009/file/ed265bc903a5a097f61d3ec064d96d2e-Paper.pdf,offline_nips,,"The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel computing devices provides us with new opportunities to develop scalable learning methods for massive data. In this work, we consider the problem of parallelizing two inference methods on GPUs for latent Dirichlet A"
216,d95426a57d,Simulation of the Neocognitron on a CCD Parallel Processing Architecture,Michael L. Chuang; Alice M. Chiang,1990,NIPS 1990,main,Poster,,,0,0.591,0.000,,https://papers.nips.cc/paper_files/paper/1990/hash/17d63b1625c816c22647a73e1482372b-Abstract.html,https://papers.nips.cc/paper_files/paper/1990/file/17d63b1625c816c22647a73e1482372b-Paper.pdf,offline_nips,,The neocognitron  is  a  neural network for  pattern recognition  and feature  extraction.  An  analog  CCD  parallel  processing  architecture  developed  at Lincoln Laboratory is  particularly  well suited to the computational re(cid:173) quirements of shared-weight networks such as the neocognitr