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Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environments. While recent video generation models have shown strong potential in modeling dynamic scenes,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Zeyi Liu , Shuang Li , Eric Cousineau , Siyuan Feng , Benjamin Burchfiel , Shuran Song

Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yisu Zhang , Chenjie Cao , Tengfei Wang , Xuhui Zuo , Junta Wu , Jianke Zhu , Chunchao Guo

Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result, the generated scenes are often object-centric and…

Computer Vision and Pattern Recognition · Computer Science 2024-11-22 Heng Yu , Chaoyang Wang , Peiye Zhuang , Willi Menapace , Aliaksandr Siarohin , Junli Cao , Laszlo A Jeni , Sergey Tulyakov , Hsin-Ying Lee

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

The advancement of 4D (i.e., sequential 3D) generation opens up new possibilities for lifelike experiences in various applications, where users can explore dynamic objects or characters from any viewpoint. Meanwhile, video generative models…

Graphics · Computer Science 2025-04-08 Yikai Wang , Guangce Liu , Xinzhou Wang , Zilong Chen , Jiafang Li , Xin Liang , Fuchun Sun , Jun Zhu

Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As a result, the generated videos appear plausible, yet lack the physical grounding…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Kaichen Zhou , Yuzhen Chen , Fangneng Zhan , Hang Hua , Grace Chen , Xinhai Chang , Ao Qu , Yilun Du , Zhuang Liu , Paul Pu Liang , Mengyu Wang

Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Yuheng Liu , Xin Lin , Xinke Li , Baihan Yang , Chen Wang , Kalyan Sunkavalli , Yannick Hold-Geoffroy , Hao Tan , Kai Zhang , Xiaohui Xie , Zifan Shi , Yiwei Hu

Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art generative models operate most effectively in a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Hanyang Kong , Xingyi Yang , Xiaoxu Zheng , Xinchao Wang

World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent video generation models have demonstrated impressive visual…

World models serve as essential building blocks toward Artificial General Intelligence (AGI), enabling intelligent agents to predict future states and plan actions by simulating complex physical interactions. However, existing interactive…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Junyi Chen , Haoyi Zhu , Xianglong He , Yifan Wang , Jianjun Zhou , Wenzheng Chang , Yang Zhou , Zizun Li , Zhoujie Fu , Jiangmiao Pang , Tong He

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Jiraphon Yenphraphai , Ashkan Mirzaei , Jianqi Chen , Jiaxu Zou , Sergey Tulyakov , Raymond A. Yeh , Peter Wonka , Chaoyang Wang

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

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate…

Computer Vision and Pattern Recognition · Computer Science 2023-05-31 Benjamin Attal , Jia-Bin Huang , Christian Richardt , Michael Zollhoefer , Johannes Kopf , Matthew O'Toole , Changil Kim

We introduce UniReal, a unified framework designed to address various image generation and editing tasks. Existing solutions often vary by tasks, yet share fundamental principles: preserving consistency between inputs and outputs while…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Xi Chen , Zhifei Zhang , He Zhang , Yuqian Zhou , Soo Ye Kim , Qing Liu , Yijun Li , Jianming Zhang , Nanxuan Zhao , Yilin Wang , Hui Ding , Zhe Lin , Hengshuang Zhao

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Sixiao Zheng , Minghao Yin , Wenbo Hu , Xiaoyu Li , Ying Shan , Yanwei Fu

We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Tianqi Liu , Zihao Huang , Zhaoxi Chen , Guangcong Wang , Shoukang Hu , Liao Shen , Huiqiang Sun , Zhiguo Cao , Wei Li , Ziwei Liu

Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spatiotemporal scale. Typically, existing 4D generative models directly embed macro scale…

Computer Vision and Pattern Recognition · Computer Science 2026-05-11 Haonan Wang , Hanyu Zhou , Tao Gu , Luxin Yan

World Generation Models are emerging as a cornerstone of next-generation multimodal intelligence systems. Unlike traditional 2D visual generation, World Models aim to construct realistic, dynamic, and physically consistent 3D/4D worlds from…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yiting Lu , Wei Luo , Peiyan Tu , Haoran Li , Hanxin Zhu , Zihao Yu , Xingrui Wang , Xinyi Chen , Xinge Peng , Xin Li , Zhibo Chen

We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produce a plausible sequence that is consistent with a text (T2V)…

Computer Vision and Pattern Recognition · Computer Science 2026-04-22 Gene Chou , Charles Herrmann , Kyle Genova , Boyang Deng , Songyou Peng , Bharath Hariharan , Jason Y. Zhang , Noah Snavely , Philipp Henzler

We present PanoWorld, a panoramic video world model that generates geometry-consistent 360$\degree$ video from a single image and a caption. Existing panoramic video methods optimize primarily for visual realism and do not explicitly…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Le Jiang , Xiangyu Bai , Bishoy Galoaa , Shayda Moezzi , Caleb James Lee , Tooba Imtiaz , Edmund Yeh , Jennifer Dy , Yanzhi Wang , Sarah Ostadabbas
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