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Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challenging due to accumulated errors, motion drift, and content…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yifei Yu , Xiaoshan Wu , Xinting Hu , Tao Hu , Yangtian Sun , Xiaoyang Lyu , Bo Wang , Lin Ma , Yuewen Ma , Zhongrui Wang , Xiaojuan Qi

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…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Yu Shang , Lei Jin , Yiding Ma , Xin Zhang , Chen Gao , Wei Wu , Yong Li

Recent video generators achieve striking photorealism, yet remain fundamentally inconsistent in 3D. We present WorldReel, a 4D video generator that is natively spatio-temporally consistent. WorldReel jointly produces RGB frames together…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Shaoheng Fang , Hanwen Jiang , Yunpeng Bai , Niloy J. Mitra , Qixing Huang

For recent diffusion-based generative models, maintaining consistent content across a series of generated images, especially those containing subjects and complex details, presents a significant challenge. In this paper, we propose a new…

Computer Vision and Pattern Recognition · Computer Science 2024-05-03 Yupeng Zhou , Daquan Zhou , Ming-Ming Cheng , Jiashi Feng , Qibin Hou

Streaming video generation, as one fundamental component in interactive world models and neural game engines, aims to generate high-quality, low-latency, and temporally coherent long video streams. However, most existing work suffers from…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Kunhao Liu , Wenbo Hu , Jiale Xu , Ying Shan , Shijian Lu

Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Zun Wang , Han Lin , Jaehong Yoon , Jaemin Cho , Yue Zhang , Mohit Bansal

Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zicheng Duan , Jiatong Xia , Zeyu Zhang , Wenbo Zhang , Gengze Zhou , Chenhui Gou , Yefei He , Feng Chen , Xinyu Zhang , Lingqiao Liu

Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user control over the environment for reproducible, editable…

Artificial Intelligence · Computer Science 2026-04-01 Ryan Po , David Junhao Zhang , Amir Hertz , Gordon Wetzstein , Neal Wadhwa , Nataniel Ruiz

Dynamical systems theory and reinforcement learning view world evolution as latent-state dynamics driven by actions, with visual observations providing partial information about the state. Recent video world models attempt to learn this…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zhen Li , Zian Meng , Shuwei Shi , Wenshuo Peng , Yuwei Wu , Bo Zheng , Chuanhao Li , Kaipeng Zhang

Recent approaches have demonstrated the promise of using diffusion models to generate interactive and explorable worlds. However, most of these methods face critical challenges such as excessively large parameter sizes, reliance on lengthy…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Xiaofeng Mao , Zhen Li , Chuanhao Li , Xiaojie Xu , Kaining Ying , Tong He , Jiangmiao Pang , Yu Qiao , Kaipeng Zhang

The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solutions maintain the memory by compressing historical frames with…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Sihui Ji , Xi Chen , Shuai Yang , Xin Tao , Pengfei Wan , Hengshuang Zhao

Building world models with spatial consistency and real-time interactivity remains a fundamental challenge in computer vision. Current video generation paradigms often struggle with a lack of spatial persistence and insufficient visual…

Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Qihang Zhang , Shuangfei Zhai , Miguel Angel Bautista , Kevin Miao , Alexander Toshev , Joshua Susskind , Jiatao Gu

Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared world modeling, where a model generates multiple videos from a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Fan Wu , Jiacheng Wei , Ruibo Li , Yi Xu , Junyou Li , Deheng Ye , Guosheng Lin

World models aim to predict plausible futures consistent with past observations, a capability central to planning and decision-making in reinforcement learning. Yet, existing architectures face a fundamental memory trade-off: transformers…

Machine Learning · Computer Science 2026-05-20 Sebastian Stapf , Pablo Acuaviva Huertos , Aram Davtyan , Paolo Favaro

Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified understanding of the world. Prior approaches typically incorporate only a single form of…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Boming Tan , Xiangdong Zhang , Ning Liao , Yuqing Zhang , Shaofeng Zhang , Xue Yang , Qi Fan , Yanyong Zhang

World modeling is a crucial task for enabling intelligent agents to effectively interact with humans and operate in dynamic environments. In this work, we propose MineWorld, a real-time interactive world model on Minecraft, an open-ended…

Computer Vision and Pattern Recognition · Computer Science 2025-04-14 Junliang Guo , Yang Ye , Tianyu He , Haoyu Wu , Yushu Jiang , Tim Pearce , Jiang Bian

Recent advancements in video generation have primarily leveraged diffusion models for short-duration content. However, these approaches often fall short in modeling complex narratives and maintaining character consistency over extended…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Canyu Zhao , Mingyu Liu , Wen Wang , Weihua Chen , Fan Wang , Hao Chen , Bo Zhang , Chunhua Shen

Real-world applications like video gaming and virtual reality often demand the ability to model 3D scenes that users can explore along custom camera trajectories. While significant progress has been made in generating 3D objects from text…

Computer Vision and Pattern Recognition · Computer Science 2025-06-05 Tianyu Huang , Wangguandong Zheng , Tengfei Wang , Yuhao Liu , Zhenwei Wang , Junta Wu , Jie Jiang , Hui Li , Rynson W. H. Lau , Wangmeng Zuo , Chunchao Guo

We introduce LivingWorld, an interactive framework for generating 4D worlds with environmental dynamics from a single image. While recent advances in 3D scene generation enable large-scale environment creation, most approaches focus…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Hyeongju Mun , In-Hwan Jin , Sohyeong Kim , Kyeongbo Kong