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We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. Current works fail at this task because their physical state is decoupled from their visual…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Jiahao Zhan , Zizhang Li , Hong-Xing Yu , Jiajun Wu

In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-01-12 Qihao Liu , Ju He , Qihang Yu , Liang-Chieh Chen , Alan Yuille

We present PhysGen, a novel image-to-video generation method that converts a single image and an input condition (e.g., force and torque applied to an object in the image) to produce a realistic, physically plausible, and temporally…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Shaowei Liu , Zhongzheng Ren , Saurabh Gupta , Shenlong Wang

WonderPlay is a novel framework integrating physics simulation with video generation for generating action-conditioned dynamic 3D scenes from a single image. While prior works are restricted to rigid body or simple elastic dynamics,…

Graphics · Computer Science 2025-12-02 Zizhang Li , Hong-Xing Yu , Wei Liu , Yin Yang , Charles Herrmann , Gordon Wetzstein , Jiajun Wu

State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Furthermore, without an underlying explicit geometry, these…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Dana Cohen-Bar , Ido Sobol , Raphael Bensadoun , Shelly Sheynin , Oran Gafni , Or Patashnik , Daniel Cohen-Or , Amit Zohar

While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide the video…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Haoze Zhang , Tianyu Huang , Zichen Wan , Xiaowei Jin , Hongzhi Zhang , Hui Li , Wangmeng Zuo

World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation models are emerging as a promising basis for such simulators because they can generate diverse…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Pu Zhao , Juyi Lin , Timothy Rupprecht , Arash Akbari , Chence Yang , Rahul Chowdhury , Elaheh Motamedi , Arman Akbari , Yumei He , Chen Wang , Geng Yuan , Weiwei Chen , Yanzhi Wang

Generating human videos with realistic and controllable motions is a challenging task. While existing methods can generate visually compelling videos, they lack separate control over four key video elements: foreground subject, background…

Computer Vision and Pattern Recognition · Computer Science 2025-08-13 Jingyun Liang , Jingkai Zhou , Shikai Li , Chenjie Cao , Lei Sun , Yichen Qian , Weihua Chen , Fan Wang

Recent advances in video generation have shown promise for generating future scenarios, critical for planning and control in autonomous driving and embodied intelligence. However, real-world applications demand more than visually plausible…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Tianshuo Xu , Zhifei Chen , Leyi Wu , Hao Lu , Yuying Chen , Lihui Jiang , Bingbing Liu , Yingcong Chen

The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence…

Artificial Intelligence · Computer Science 2026-02-09 Jingtong Yue , Ziqi Huang , Zhaoxi Chen , Xintao Wang , Pengfei Wan , Ziwei Liu

Current motion-conditioned video generation methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present MotionStream, enabling sub-second latency with up to 29 FPS…

Computer Vision and Pattern Recognition · Computer Science 2026-03-06 Joonghyuk Shin , Zhengqi Li , Richard Zhang , Jun-Yan Zhu , Jaesik Park , Eli Shechtman , Xun Huang

Camera control has been actively studied in text or image conditioned video generation tasks. However, altering camera trajectories of a given video remains under-explored, despite its importance in the field of video creation. It is…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Jianhong Bai , Menghan Xia , Xiao Fu , Xintao Wang , Lianrui Mu , Jinwen Cao , Zuozhu Liu , Haoji Hu , Xiang Bai , Pengfei Wan , Di Zhang

Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification through task-agnostic data and generation, existing visual…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Yijing Lin , Mengqi Huang , Shuhan Zhuang , Zhendong Mao

We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic visual demonstrations from language commands and images,…

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Cong Wang , Jiaxi Gu , Panwen Hu , Songcen Xu , Hang Xu , Xiaodan Liang

Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce PhysGen3D, a novel framework that transforms a single image into an amodal, camera-centric,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Boyuan Chen , Hanxiao Jiang , Shaowei Liu , Saurabh Gupta , Yunzhu Li , Hao Zhao , Shenlong Wang

Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically meaningful interactions that mimic real-world forces remain…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Nate Gillman , Charles Herrmann , Michael Freeman , Daksh Aggarwal , Evan Luo , Deqing Sun , Chen Sun

Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned…

Computer Vision and Pattern Recognition · Computer Science 2024-10-08 Tianyuan Zhang , Hong-Xing Yu , Rundi Wu , Brandon Y. Feng , Changxi Zheng , Noah Snavely , Jiajun Wu , William T. Freeman

Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse control signals such as text or keyboard input, limiting their utility for embodied…

Computer Vision and Pattern Recognition · Computer Science 2026-02-23 Linxi Xie , Lisong C. Sun , Ashley Neall , Tong Wu , Shengqu Cai , Gordon Wetzstein

Producing long, coherent video sequences with stable 3D structure remains a major challenge, particularly in streaming scenarios. Motivated by this, we introduce Endless World, a real-time framework for infinite, 3D-consistent video…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Ke Zhang , Yiqun Mei , Jiacong Xu , Vishal M. Patel
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