Temporal Consistency in Video Generation

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30 papers in the last four weeks, up 275% on the four weeks before. 0.3% of all new papers.

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DeltaCam: Differential Intrinsic Camera Modeling for Video Generation
May 24, 2026Debabrata Mandal, Zhihan Peng, Yujie Wang +1Camera-Conditioned Video Generation Video Diffusion Models
UNC, Chapel Hill, USA
DexSIM: Real-time Dexterous Simulation with Unified Causal Video Diffusion
May 23, 2026Adam LeeVideo Diffusion Models Real-Time Interactive Video Generation
UC Berkeley
EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation
May 22, 2026Jente Vandersanden, Matheus Gadelha, Chun-Hao P. Huang +2Memory-Augmented Video Generation Identity-Preserving Video Generation
Max Planck Institute for Informatics, Germany and Adobe Research, UK · Adobe Research, UK · Adobe Research, USA
SimInsert: Seamless Video Object Insertion via Regional Sparse Attention Fusion
May 22, 2026Xinyu Chen, Yuyi Qian, Jiang Lin +9Video Diffusion Models Video Editing
1State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China · School of Intelligence Science and Technology, Nanjing University, Suzhou, China · 4Xi’an Jiaotong-Liverpool University +3
WorldKV: Efficient World Memory with World Retrieval and Compression
May 21, 2026Jung Yi, Minjae Kim, Paul Hyunbin Cho +3KV Caching Memory-Augmented Video Generation
1KAIST AI · 2Naver AI Lab
Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain
May 21, 2026Francesco Benedetto, Roberto Basla, Luca Magri +1Synthetic Data Generation Video Generation
Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
BodyReLux: Temporally Consistent Full-Body Video Relighting
May 20, 2026Li Ma, Mingming He, Xueming Yu +4Video Diffusion Models Controllable Video Generation
Eyeline Labs, United States of America · Eyeline Labs, Canada · Eyeline Labs +2
GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
May 20, 2026Kaichen Zhou, Yuzhen Chen, Fangneng Zhan +8Physical Consistency in Video Generation Robot Motion Generation
Harvard AI and Robotics Lab, Harvard University · Media Lab and EECS, MIT · MIT-IBM Watson AI Lab +1
FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching
May 20, 2026Jangho Park, Geon Yeong Park, Gihyun Kwon +1Video Diffusion Models Long Video Generation
1KAIST · 2Amazon
PhyWorld: Physics-Faithful World Model for Video Generation
May 19, 2026Pu Zhao, Juyi Lin, Timothy Rupprecht +10Physics-Informed Generative Modeling Physical Consistency in Video Generation
Northeastern University · University of Georgia · Tulane University +1
Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
May 18, 2026X. Feng, J. Zhu, M. Wu +6Video Diffusion Models Long Video Generation
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · AMAP, Alibaba Group, Beijing, China.
Temporal Aware Pruning for Efficient Diffusion-based Video Generation
May 18, 2026Sheng Li, Yang Sui, Junhao Ran +3Video Diffusion Models Token Pruning
University of Pittsburgh · Rice University · Rutgers University +1
HL-OutPaint: Coarse-to-Fine Video Outpainting for High-Resolution Long-Range Videos
May 17, 2026Jeongeun Park, Janghyeok Han, Geonung Kim +4Long Video Generation Video Generation
POSTECH, Republic of Korea · Visual Display Business, Samsung Electronics, Republic of Korea
Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration
May 17, 2026Yiren Song, Huilin Zhong, Kevin Qinghong Lin +2Video-Reference Video Generation Long-Horizon Video Generation
Show Lab, National University of Singapore · University of Oxford · Lovart AI
VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers
May 17, 2026Yiren Song, Wangzi Yao, Haofan Wang +1Video Diffusion Models Temporal Consistency in Video Generation
Show Lab, National University of Singapore · Institute of Automation, Chinese Academy of Sciences · Lovart AI
Image-to-Video Diffusion: From Foundations to Open Frontiers
May 17, 2026Xianlong Wang, Wenbo Pan, Shijia Zhou +6Image-to-Video Generation Video Diffusion Models
Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China · School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China · School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China +3
Attend Locally, Remember Linearly: Linear Attention as Cross-Frame Memory for Autoregressive Video Diffusion
May 15, 2026Kunyang Li, Mubarak Shah, Yuzhang ShangMemory-Augmented Neural Networks Video Diffusion Models
Institute of Artificial Intelligence, University of Central Florida
Evaluating Design Video Generation: Metrics for Compositional Fidelity
May 15, 2026Adrienne Deganutti, Dingning Cao, Jaejung Seol +2Temporal Consistency in Video Generation Video Generation Evaluation
Lica World, San Francisco, United States of America.
FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization
May 15, 2026Quanjian Song, Yefeng Shen, Mengting Chen +5Virtual Try-On Streaming Video Generation
Xiamen University · Alibaba Group
PanoWorld: Geometry-Consistent Panoramic Video World Modeling
May 14, 2026Le Jiang, Xiangyu Bai, Bishoy Galoaa +7Physical Consistency in Video Generation Video Generation
Northeastern University
RefDecoder: Enhancing Visual Generation with Conditional Video Decoding
May 14, 2026Xiang Fan, Yuheng Wang, Bohan Fang +2Image-to-Video Generation Conditional Variational Autoencoder
University of Washington · University of North Carolina at Chapel Hill
EverAnimate: Minute-Scale Human Animation via Latent Flow Restoration
May 14, 2026Wuyang Li, Yang Gao, Mariam Hassan +4Flow Matching Long-Horizon Video Generation
Video Reconstruction using Diffusion-based Image-to-Video Generation with Trajectory Guidance
May 14, 2026Stelio Bompai, Ioannis Kontopoulos, Giannis Spiliopoulos +2Image-to-Video Generation Video Diffusion Models
Department of Electrical and Computer Engineering, National Technical University of Athens, Greece · Department of Product and Systems Design Engineering, University of the Aegean, Syros, Greece
Head Forcing: Long Autoregressive Video Generation via Head Heterogeneity
May 14, 2026Jiahao Tian, Yiwei Wang, Gang Yu +1Diffusion Transformer Autoregressive Diffusion
AGI Lab, Westlake University · University of California at Merced · StepFun
Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation
May 14, 2026Yuheng Wu, Xiangbo Gao, Tianhao Chen +4Real-Time Interactive Video Generation Streaming Video Generation
1KAIST · *Work done when intern at Visko · 3Texas A&M University +3
TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion
May 13, 2026Nurislam Tursynbek, Zhiqiang Lao, Heather Yu +2Video Diffusion Models Diffusion Transformer
UNC Chapel Hill1 · Futurewei Technologies Inc2 · UCSD3
Pyramid Forcing: Head-Aware Pyramid KV Cache Policy for High-Quality Long Video Generation
May 13, 2026Jiayu Chen, Junbei Tang, Wenbiao Zhao +6Self-Attention Long-Horizon Video Generation
Peking University · South China University of Technology · Xinjiang University +2
CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
May 12, 2026Yihao Meng, Zichen Liu, Hao Ouyang +11Real-Time Interactive Video Generation Long-Horizon Video Generation
1HKUST · 2Ant Group · 3SJTU
RealDiffusion: Physics-informed Attention for Multi-character Storybook Generation
May 12, 2026Qi Zhao, Jun Chen, Ivor Tsang +1Diffusion-Based Image Generation Temporal Consistency in Video Generation
Xi’an Jiaotong University, China · Zhejiang Normal University, China · SGIT AI Lab, State Grid Corporation of China +1
Improving Human Image Animation via Semantic Representation Alignment
May 11, 2026Chang Liu, Mengting Chen, Yixuan Huang +5Image-to-Video Generation Video Diffusion Models
School of Artificial Intelligence, Shanghai Jiao Tong University, China · Alibaba Group, China

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DeltaCam: Differential Intrinsic Camera Modeling for Video Generation

Debabrata Mandal, Zhihan Peng, Yujie Wang, Praneeth Chakravarthula

UNC, Chapel Hill, USA

Camera-Conditioned Video Generation Video Diffusion Models Controllable Video Generation Temporal Consistency in Video Generation

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Incorporating camera intrinsics into video generation models offers a principled way to control not only scene dynamics but also the imaging process that governs visual appearance. Prior work has primarily focused on extrinsic control, such as camera pose and motion, while treating intrinsic camera parameters as implicit or fixed. A key bottleneck is the lack of large-scale video datasets with accurate and diverse temporally varying camera metadata, which makes learning absolute camera parameterizations difficult. As a result, current models struggle to incorporate photographic camera behavior, including depth-of-field transitions, exposure variations, lens distortions, and color processing, in a controllable and temporally consistent manner. We introduce DeltaCam, a video diffusion framework that models camera behavior through

Δ

-parameterized neural camera adaptors, operating on relative changes in camera motion and intrinsics instead of absolute states. By learning this differential formulation from synthetic video data, we mitigate reliance on precise real-world camera labels and enable smooth, consistent control over imaging factors such as focal length, aperture, ISO, color temperature, and lens distortion. We extend this framework to real-world footage through two mechanisms: finetuning the controls on real image-metadata pairs for precise shot matching, and extracting disentangled embeddings for implicit video-to-video style transfer without requiring explicit camera parameters. By effectively separating scene content from intrinsic imaging behavior, DeltaCam enables camera-consistent video generation and editing operations that are difficult to achieve with existing models. Ultimately, our results establish a practical and scalable approach for bridging synthetic control and real-world photographic emulation.

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Latest papers 224