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We propose Stable Video Infinity (SVI) that is able to generate infinite-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines. While existing long-video methods attempt to mitigate…

Computer Vision and Pattern Recognition · Computer Science 2025-10-13 Wuyang Li , Wentao Pan , Po-Chien Luan , Yang Gao , Alexandre Alahi

We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operates on a geometric scaffold computed via…

Computer Vision and Pattern Recognition · Computer Science 2021-05-04 Gernot Riegler , Vladlen Koltun

Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Zichen Liu , Kunlun Xu , Bing Su , Xu Zou , Yuxin Peng , Jiahuan Zhou

Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Willi Menapace , Aliaksandr Siarohin , Ivan Skorokhodov , Ekaterina Deyneka , Tsai-Shien Chen , Anil Kag , Yuwei Fang , Aleksei Stoliar , Elisa Ricci , Jian Ren , Sergey Tulyakov

We present a novel framework for compositional video synthesis that leverages temporally consistent object-centric representations, extending our previous work, SlotAdapt, from images to video. While existing object-centric approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-07-29 Adil Kaan Akan , Yucel Yemez

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a…

Computer Vision and Pattern Recognition · Computer Science 2022-03-17 Willi Menapace , Stéphane Lathuilière , Aliaksandr Siarohin , Christian Theobalt , Sergey Tulyakov , Vladislav Golyanik , Elisa Ricci

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

Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of domains including spatial intelligence, embodied intelligence, and autonomous driving. While…

Computer Vision and Pattern Recognition · Computer Science 2026-05-14 Hanxin Zhu , Cong Wang , Peiyan Tu , Jiayi Luo , Tianyu He , Xin Jin , Zhibo Chen

Deep neural networks, especially transformer-based architectures, have achieved remarkable success in semantic segmentation for environmental perception. However, existing models process video frames independently, thus failing to leverage…

Computer Vision and Pattern Recognition · Computer Science 2026-03-23 Serin Varghese , Kevin Ross , Fabian Hueger , Kira Maag

Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Zixun Fang , Kai Zhu , Zhiheng Liu , Yu Liu , Wei Zhai , Yang Cao , Zheng-Jun Zha

Diffusion models have achieved great success in image generation. However, when leveraging this idea for video generation, we face significant challenges in maintaining the consistency and continuity across video frames. This is mainly…

Computer Vision and Pattern Recognition · Computer Science 2024-03-25 Haoran Lang , Yuxuan Ge , Zheng Tian

Creating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G$^{3}$AN, a novel spatio-temporal generative model, which seeks to…

Computer Vision and Pattern Recognition · Computer Science 2020-06-16 Yaohui Wang , Piotr Bilinski , Francois Bremond , Antitza Dantcheva

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short…

Computer Vision and Pattern Recognition · Computer Science 2019-09-18 Qiangeng Xu , Hanwang Zhang , Weiyue Wang , Peter N. Belhumeur , Ulrich Neumann

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficiencies in achieving spatiotemporal consistency, resulting in…

Computer Vision and Pattern Recognition · Computer Science 2023-10-24 Haipeng Fang , Zhihao Sun , Ziyao Huang , Fan Tang , Juan Cao , Sheng Tang

This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction. ShareVerse…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Jiayi Zhu , Jianing Zhang , Yiying Yang , Wei Cheng , Xiaoyun Yuan

Recent advancements in video generation have substantially improved visual quality and temporal coherence, making these models increasingly appealing for applications such as autonomous driving, particularly in the context of driving…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Chun-Peng Chang , Chen-Yu Wang , Julian Schmidt , Holger Caesar , Alain Pagani

Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via feed-forward reconstruction techniques. This generative…

High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense…

Computer Vision and Pattern Recognition · Computer Science 2026-04-08 Weihong Pan , Xiaoyu Zhang , Zhuang Zhang , Zhichao Ye , Nan Wang , Haomin Liu , Guofeng Zhang

Stable Diffusion models have made remarkable strides in generating photorealistic images from text prompts but often falter when tasked with accurately representing complex spatial arrangements, particularly involving intricate 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-02-25 Mao Xun Huang , Brian J Chan , Hen-Hsen Huang

AI video generation is evolving rapidly. For video generators to be useful for applications ranging from robotics to film-making, they must consistently produce realistic videos. However, evaluating the realism of generated videos remains a…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Bhavik Chandna , Kelsey R. Allen