Latest Video Generation Research Papers

The newest Video Generation papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Video Generation so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Video Generation Meets Agent Harness: Agentic Video Generation, Agentic World Modeling and Recursive Self-Improvement
  Liaoyuan Fan, Yuhao Zheng, Dongxing Mao, Hangyu Ran et al. · arXiv · Oct 29, 2026

  Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, and feedback enable coherent, controllable multi-shot videos. In parallel, video world models provide action-conditioned observation…

• Repurposing Video Diffusion Transformers for Cross-View Temporal Object Correspondence
  Siyoon Jin, Dahyun Chung, Honggyu An, Sangbeom Lim et al. · arXiv · Oct 27, 2026

  Cross-view object correspondence identifies the same object across views. However, existing methods often rely on frame-wise matching or sequential tracking, which can propagate correspondence errors under appearance changes or occlusions. …

• ReferStream: Interactive Subject Injection for Streaming Video Generation
  Shuolin Xu, Guanyuan Li, Boyu Li, Kelu Yao et al. · arXiv · Oct 27, 2026

  Injecting new subjects naturally during streaming video generation requires integrating newly supplied visual references into the existing video while preserving established subjects and the scene. Encoding references independently of the c…

• GroundControl: Satellite-Conditioned Multi-View Diffusion for Reconstruction-Free Novel View Synthesis
  Akshay Krishnan, Matias Turkulainen, Jamie Wynn, Zawar Qureshi et al. · arXiv · Oct 27, 2026

  Multi-view or video diffusion models have recently emerged as state-of-the-art approaches for novel-view synthesis from sparse image collections. While they can produce high quality images, they tend to hallucinate unrealistic spatial layou…

• Taming Noise Initialization for Zero-shot Video Restoration with Image Diffusion Priors
  Chong Wang, Zixuan Fu, Hao Cheng, Siyuan Yang et al. · arXiv · Oct 27, 2026

  Recent advances in diffusion models have achieved remarkable success in restoring high-quality images in a zero-shot manner. Unfortunately, extending this success to video restoration is challenging due to the substantial computational reso…

• VibeVideoBench:Evaluating Multimodal Agents on Code-Driven Video Generation
  Muxi Diao, Wenbin Xu, Lele Yang, Xuyang Ma et al. · arXiv · Oct 26, 2026

  Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a ruleconsistent game or narration synchronized to on-screen events. A different route is emerging: multimodal agents that wri…

• VidEdit3D: Flexible 3D Gaussian Editing with Video Diffusion
  Yanming Yang, Chenxi Song, Tong Zhao, Juntao Xu et al. · arXiv · Oct 26, 2026

  Recent advancements in text-guided 3D editing, particularly with 3D Gaussian Splatting (3DGS), have shown promise but still lag behind the flexibility of 2D editing tools. Existing methods face a critical trade-off between preserving multi-…

• Triage: Skip, Skim, or Refine Attention Tiles in Video Diffusion Transformers
  Yeonjae Kim, Omin Kwon, Seong Hoon Seo, Jae W. Lee · arXiv · Oct 26, 2026

  Attention over tens of thousands of visual tokens dominates the inference of video diffusion transformers (DiTs). Existing methods reduce this cost through sparsity, low precision, or both, but use a single precision for all kept tiles. We …

• OmniShowcase: Omnidirectional Subject-Consistent Video Generation from Multi-View Images
  Junyeong Ahn, Geonwoo Kim, Kinam Kim, Gihwi Kim et al. · arXiv · Oct 26, 2026

  Generating videos of a specific subject from a few reference images remains challenging when the camera reveals surfaces that are not visible in the input. Early subject-to-video (S2V) methods condition on a single reference image and must …

• VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
  Richard Fechner, Peer Neubert · arXiv · Oct 26, 2026

  Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the…

• Beyond Distribution Matching: Self-Supervised Representation Forcing for Few-Step Video Generation
  Junyi Chen, Haiyu Zhang, Zhoujie Fu, Tengfei Wang et al. · arXiv · Oct 26, 2026

  Few-step distillation is essential for deploying modern video diffusion models. Among existing approaches, Distribution Matching Distillation (DMD) has emerged as a highly promising paradigm by achieving strong output-space fidelity without…

• UniWorld: A Unified World Model for Real-Time Visual World Simulation
  Xin Zhang, Yabo Chen, Shuaiwei Wang, Zixuan Duan et al. · arXiv · Oct 23, 2026

  Continuous visual world simulation requires coupling evolving scene geometry with temporally persistent appearance. We present UniWorld, a unified framework that integrates depth trajectories and reference appearance within a shared video g…

• WorldForge V2: Eliciting Camera-Trajectory Alignment in Video Diffusion Models
  Chenxi Song, Yanming Yang, Tong Zhao, Ruibo Li et al. · arXiv · Oct 22, 2026

  Video diffusion models have learned rich 3D/4D priors, making them promising for scene generation and video re-rendering. However, their use in spatial tasks is limited by imprecise camera control, spatial and temporal inconsistencies, and …

• AnyGameRenderer: Entity-Consistent Generative Rendering Across Games and an Entity-Level Benchmark
  Ruicong Liu, Liangyang Ouyang, Fengbo Lan, Zhixiang Wang et al. · arXiv · Oct 22, 2026

  AI coding agents can now build small playable games in minutes, but the results usually look like programmer art: gray blockouts, colored geometric primitives, and flat semantic blobs. We present AnyGameRenderer, a video-to-video generative…

• Rethinking Video Autoencoders for Interactive World Models
  Adrien Ramanana Rahary, Yutong Bai, Nicolas Dufour, Patrick Pérez et al. · arXiv · Oct 21, 2026

  Video world models predict future frames from past frames and actions. They serve as simulators in which agents train, plan or are evaluated, and that humans can control interactively. They generate in the latent space of a video autoencode…

• Attn-Forcing: Learning Full-Context Attention Proxies for Streaming Video Generation
  Junyao Gao, Sibo Liu, Jiaxing Li, Yanan Sun et al. · arXiv · Oct 21, 2026

  Increasing the attention context length significantly improves streaming video generation quality, while incurring prohibitive memory and computational costs as the context grows. Existing methods attempt to capture this contextual informat…

• Looking Ahead: Modeling Future Evolution in Autoregressive Video Generation
  Ruopeng Gao, Yukun Huang, Jiarui Hu, Shuai Wang et al. · arXiv · Oct 21, 2026

  Autoregressive video diffusion models generate videos chunk by chunk, yet producing plausible continuations does not guarantee that they capture how scenes evolve. Under teacher forcing, strong temporal redundancy may allow local visual con…

• CausalAlign: Causal Alignment of Video Generation Models for Consistent Ego-Motion
  Kai Zhu, Yintianrun Zhang, Shuwei Jiang, Ruolin Zuo et al. · arXiv · Oct 21, 2026

  Diffusion-based video generation models (VGMs) can produce visually plausible videos, yet their camera ego-motion often drifts over long horizons. Camera ego-motion determines how the viewpoint changes between frames and is essential for ma…

• Compositionality with Primitives: A Unified View from Euclidean to Hyperbolic Vision-Language Models
  Puru Vaish, Meelad Dashti, Christoph Brune, Pascal Mettes et al. · arXiv · Oct 21, 2026

  Compositional zero-shot learning (CZSL) aims to recognize novel combinations of previously observed objects and attributes. Prior work showed that large-scale vision-language models encode decomposable semantic structures that can be recomp…

• CineEvolve: An Agentic Self-Evolution Framework for Cinematic Multi-Shot Video Generation
  Jingzhe Zhang, Peixuan Zhang, Genying Li, Bing Ma et al. · arXiv · Oct 21, 2026

  Cinematic multi-shot video generation requires both narrative planning and iterative refinement. Existing methods either lack narrative reasoning or rely on feed-forward workflows without policy updates. In this paper, we propose CineEvolve…

• Joints, Camera, Action! Co-Generating 3D Motion and Video for Arbitrary Rigs
  Noam Rotstein, Yoad Tewel, Chen Tessler, Haggai Maron et al. · arXiv · Oct 20, 2026

  Video generation models can now depict remarkably realistic motion. Yet it is unclear how to use this motion beyond pixels: to animate a specific 3D asset and replay the animation in its 3D scene. For instance, a video model can show a monk…

• AEON: Unifying Video and 3D World Models
  Weiqi Zhang, Wenyuan Zhang, Yu-Shen Liu, Junsheng Zhou · arXiv · Oct 20, 2026

  Current world models for explicit world simulation typically follow two distinct pathways: (1) video generative models (e.g., Sora), which simulate realistic appearances but fail to preserve 3D consistency; and (2) 3D generative models (e.g…

• Copyright Leakage Detection in AI Generated Videos
  Shaif Chowdhury, Moshiur Rahman Tonmoy · arXiv · Oct 20, 2026

  Video generation models can create realistic-looking video footage from images and text prompts. Some of the recent advances have raised concerns about the reproduction of copyrighted visual content. However, copyright leakage detection in …

• Latent Bridge: From Video Latents to 3D Geometry via Feature Alignment
  Shuai Zhang, Zhanpeng Ouyang, Zhengqing Chen, Ruohong Mei et al. · arXiv · Oct 20, 2026

  Video generation and feed-forward 3D reconstruction typically use separate representations, requiring RGB decoding and visual re-encoding to recover geometry from generated latents. We propose Latent Bridge, which connects video generative …

• Code2Video-Compass: A Comprehensive Benchmark for General-Purpose Code-Based Video Generation across T2V, I2V, and V2V
  Zhenghan Chen, Zekai Shao, Xin Lin, Leting Cheng et al. · arXiv · Oct 20, 2026

  Code-based video generation offers fine-grained control, targeted editing, and component reuse compared with direct video generation. This approach requires multimodal large language models (MLLMs) to translate visual and temporal requireme…

• StreamEval: Evaluating Interactive Streaming Video Generation Models
  StreamEval Team · arXiv · Oct 20, 2026

  As streaming video generation advances, enabling real-time interaction with generated video is becoming increasingly important. We introduce StreamEval, a unified benchmark for interactive streaming video generation spanning five tasks: ava…

• VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token
  Sangbeom Lim, Seoung Wug Oh, Heeji Yoon, Seungryong Kim et al. · arXiv · Oct 19, 2026

  Diffusion-based video matting has recently emerged as a leading approach, leveraging video diffusion priors from internet-scale pretraining to achieve strong zero-shot generalization from limited synthetic data. However, the heavy compute o…

• Manga2Animate: An Agentic Framework for Faithful Manga Animation with Script and Storyboard Planning
  Sheng-Wei Zhang, Hsi-An Chen, Yan-Ting Liou, Chih-Hsuan Chen et al. · arXiv · Oct 19, 2026

  Adapting existing manga into coherent animation that stays faithful to the source is a valuable yet challenging task. Existing methods either access the manga mainly through text, losing its characters and art style, or feed manga pages dir…

• SpecKCast: Spectral Kalman Forecasting for Diffusion Acceleration
  Javad Rajabi, Hakki Can Karaimer, David B. Lindell, Babak Taati et al. · arXiv · Oct 19, 2026

  Diffusion Transformers (DiTs) achieve high-fidelity image and video generation, but they suffer from substantial inference costs due to repeated transformer evaluations across many denoising steps. Feature caching reduces this cost by reusi…

• Mise-en-Video: Fine-Grained 3D Control for Multi-Shot Video Generation
  Yingshuang Zou, Xiaoyu Shi, Yawen Luo, Ziang Li et al. · arXiv · Oct 19, 2026

  Recent advances in text-to-video generation have enabled high-fidelity single-shot synthesis, yet practical storytelling creation requires multi-shot videos with both strong inter-shot consistency and fine-grained controllability. Existing …