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Controllable video generation aims to synthesize video content that aligns precisely with user-provided conditions, such as text descriptions and initial images. However, a significant challenge persists in this domain: existing models…

Computer Vision and Pattern Recognition · Computer Science 2025-08-26 Peng Hu , Yu Gu , Liang Luo , Fuji Ren

Controllable video generation has emerged as a versatile tool for autonomous driving, enabling realistic synthesis of traffic scenarios. However, existing methods depend on control signals at inference time to guide the generative model…

Computer Vision and Pattern Recognition · Computer Science 2026-02-06 Mirlan Karimov , Teodora Spasojevic , Markus Braun , Julian Wiederer , Vasileios Belagiannis , Marc Pollefeys

We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometric transforms or appearance perturbations, our method…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Jinfan Zhou , Lixin Luo , Sungmin Eum , Heesung Kwon , Jeong Joon Park

We present Stable Virtual Camera (Seva), a generalist diffusion model that creates novel views of a scene, given any number of input views and target cameras. Existing works struggle to generate either large viewpoint changes or temporally…

Computer Vision and Pattern Recognition · Computer Science 2025-04-03 Jensen Zhou , Hang Gao , Vikram Voleti , Aaryaman Vasishta , Chun-Han Yao , Mark Boss , Philip Torr , Christian Rupprecht , Varun Jampani

Efficient neural representations for dynamic video scenes are critical for applications ranging from video compression to interactive simulations. Yet, existing methods often face challenges related to high memory usage, lengthy training…

Computer Vision and Pattern Recognition · Computer Science 2025-01-10 Andrew Bond , Jui-Hsien Wang , Long Mai , Erkut Erdem , Aykut Erdem

World models based on video generation demonstrate remarkable potential for simulating interactive environments but face persistent difficulties in two key areas: maintaining long-term content consistency when scenes are revisited and…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Tianxing Xu , Zixuan Wang , Guangyuan Wang , Li Hu , Zhongyi Zhang , Peng Zhang , Bang Zhang , Song-Hai Zhang

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 investigate whether video generative models can exhibit visuospatial intelligence, a capability central to human cognition, using only visual data. To this end, we present Video4Spatial, a framework showing that video diffusion models…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Zeqi Xiao , Yiwei Zhao , Lingxiao Li , Yushi Lan , Ning Yu , Rahul Garg , Roshni Cooper , Mohammad H. Taghavi , Xingang Pan

We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within…

Computer Vision and Pattern Recognition · Computer Science 2026-01-01 Zhening Huang , Hyeonho Jeong , Xuelin Chen , Yulia Gryaditskaya , Tuanfeng Y. Wang , Joan Lasenby , Chun-Hao Huang

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

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

Predicting future frames of a video is challenging because it is difficult to learn the uncertainty of the underlying factors influencing their contents. In this paper, we propose a novel video prediction model, which has…

Computer Vision and Pattern Recognition · Computer Science 2024-02-20 Xi Ye , Guillaume-Alexandre Bilodeau

World simulation has gained increasing popularity due to its ability to model virtual environments and predict the consequences of actions. However, the limited temporal context window often leads to failures in maintaining long-term…

Computer Vision and Pattern Recognition · Computer Science 2026-01-05 Zeqi Xiao , Yushi Lan , Yifan Zhou , Wenqi Ouyang , Shuai Yang , Yanhong Zeng , Xingang Pan

Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an essential component for intelligent systems. Recent approaches have improved temporal coherence…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Melonie de Almeida , Daniela Ivanova , Tong Shi , John H. Williamson , Paul Henderson

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 generation and editing conditioned on text prompts or images have undergone significant advancements. However, challenges remain in accurately controlling global layout and geometry details solely by texts, and supporting motion…

Graphics · Computer Science 2025-04-01 Feng-Lin Liu , Hongbo Fu , Xintao Wang , Weicai Ye , Pengfei Wan , Di Zhang , Lin Gao

Despite remarkable progress in video generation, maintaining long-term scene consistency upon revisiting previously explored areas remains challenging. Existing solutions rely either on explicitly constructing 3D geometry, which suffers…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Jia Li , Han Yan , Yihang Chen , Siqi Li , Xibin Song , Yifu Wang , Jianfei Cai , Tien-Tsin Wong , Pan Ji

We present a novel method for generating geometrically realistic and consistent orbital videos from a single image of an object. Existing video generation works mostly rely on pixel-wise attention to enforce view consistency across frames.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-15 Rong Wang , Ruyi Zha , Ziang Cheng , Jiayu Yang , Pulak Purkait , Hongdong Li

In this paper, we explore the overlooked challenge of stability and temporal consistency in interactive video generation, which synthesizes dynamic and controllable video worlds through interactive behaviors such as camera movements and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-22 Ying Yang , Zhengyao Lv , Tianlin Pan , Haofan Wang , Binxin Yang , Hubery Yin , Chen Li , Ziwei Liu , Chenyang Si

While video generation models excel at producing high-quality monocular videos, generating 3D stereoscopic and spatial videos for immersive applications remains an underexplored challenge. We present a pose-free and training-free method…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Peng Dai , Feitong Tan , Qiangeng Xu , Yihua Huang , David Futschik , Ruofei Du , Sean Fanello , Yinda Zhang , Xiaojuan Qi