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Generative AI has made significant strides in computer vision, particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements, it remains challenging in human-centric content synthesis such as realistic dance…

Computer Vision and Pattern Recognition · Computer Science 2024-04-08 Tan Wang , Linjie Li , Kevin Lin , Yuanhao Zhai , Chung-Ching Lin , Zhengyuan Yang , Hanwang Zhang , Zicheng Liu , Lijuan Wang

We tackle the long video generation problem, i.e.~generating videos beyond the output length of video generation models. Due to the computation resource constraints, video generation models can only generate video clips that are relatively…

Computer Vision and Pattern Recognition · Computer Science 2023-04-18 Hsin-Ping Huang , Yu-Chuan Su , Ming-Hsuan Yang

Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient…

Computer Vision and Pattern Recognition · Computer Science 2023-06-02 Jinbo Xing , Menghan Xia , Yuxin Liu , Yuechen Zhang , Yong Zhang , Yingqing He , Hanyuan Liu , Haoxin Chen , Xiaodong Cun , Xintao Wang , Ying Shan , Tien-Tsin Wong

AI video generation is undergoing a revolution, with quality and realism advancing rapidly. These advances have led to a passionate scientific debate: Do video models learn "world models" that discover laws of physics -- or, alternatively,…

Computer Vision and Pattern Recognition · Computer Science 2025-02-28 Saman Motamed , Laura Culp , Kevin Swersky , Priyank Jaini , Robert Geirhos

Videos depict the change of complex dynamical systems over time in the form of discrete image sequences. Generating controllable videos by learning the dynamical system is an important yet underexplored topic in the computer vision…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Yucheng Xu , Li Nanbo , Arushi Goel , Zijian Guo , Zonghai Yao , Hamidreza Kasaei , Mohammadreze Kasaei , Zhibin Li

Real-time interactive video-chat portraits have been increasingly recognized as the future trend, particularly due to the remarkable progress made in text and voice chat technologies. However, existing methods primarily focus on real-time…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 Jinwei Qi , Chaonan Ji , Sheng Xu , Peng Zhang , Bang Zhang , Liefeng Bo

In order to perform unconditional video generation, we must learn the distribution of the real-world videos. In an effort to synthesize high-quality videos, various studies attempted to learn a mapping function between noise and videos,…

Computer Vision and Pattern Recognition · Computer Science 2021-12-22 Kangyeol Kim , Sunghyun Park , Junsoo Lee , Joonseok Lee , Sookyung Kim , Jaegul Choo , Edward Choi

Video generators are increasingly evaluated as potential world models, which requires them to encode and understand physical laws. We investigate their representation of a fundamental law: gravity. Out-of-the-box video generators…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Varun Varma Thozhiyoor , Shivam Tripathi , Venkatesh Babu Radhakrishnan , Anand Bhattad

Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 David Romero , Ariana Bermudez , Viacheslav Iablochnikov , Hao Li , Fabio Pizzati , Ivan Laptev

World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data scarcity challenges. However, current embodied world models…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Yu Shang , Xin Zhang , Yinzhou Tang , Lei Jin , Chen Gao , Wei Wu , Yong Li

By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While…

Computer Vision and Pattern Recognition · Computer Science 2025-01-09 Maham Tanveer , Yang Zhou , Simon Niklaus , Ali Mahdavi Amiri , Hao Zhang , Krishna Kumar Singh , Nanxuan Zhao

Despite tremendous progress in dexterous manipulation, current visuomotor policies remain fundamentally limited by two challenges: they struggle to generalize under perceptual or behavioral distribution shifts, and their performance is…

Robotics · Computer Science 2025-08-04 Junbang Liang , Pavel Tokmakov , Ruoshi Liu , Sruthi Sudhakar , Paarth Shah , Rares Ambrus , Carl Vondrick

High-quality driving video generation is crucial for providing training data for autonomous driving models. However, current generative models rarely focus on enhancing camera motion control under multi-view tasks, which is essential for…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Yining Yao , Xi Guo , Chenjing Ding , Wei Wu

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this…

Computer Vision and Pattern Recognition · Computer Science 2018-08-10 Lijie Fan , Wenbing Huang , Chuang Gan , Junzhou Huang , Boqing Gong

Existing person video generation methods either lack the flexibility in controlling both the appearance and motion, or fail to preserve detailed appearance and temporal consistency. In this paper, we tackle the problem of motion transfer…

Computer Vision and Pattern Recognition · Computer Science 2019-08-13 Kun Cheng , Hao-Zhi Huang , Chun Yuan , Lingyiqing Zhou , Wei Liu

Despite recent progress, video diffusion models still struggle to synthesize realistic videos involving highly dynamic motions or requiring fine-grained motion controllability. A central limitation lies in the scarcity of such examples in…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Wonjoon Jin , Jiyun Won , Janghyeok Han , Qi Dai , Chong Luo , Seung-Hwan Baek , Sunghyun Cho

In this paper, we introduce PoseCrafter, a one-shot method for personalized video generation following the control of flexible poses. Built upon Stable Diffusion and ControlNet, we carefully design an inference process to produce…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Yong Zhong , Min Zhao , Zebin You , Xiaofeng Yu , Changwang Zhang , Chongxuan Li

Recently, talking-face video generation has received considerable attention. So far most methods generate results with neutral expressions or expressions that are implicitly determined by neural networks in an uncontrollable way. In this…

Computer Vision and Pattern Recognition · Computer Science 2022-04-14 Zipeng Ye , Zhiyao Sun , Yu-Hui Wen , Yanan Sun , Tian Lv , Ran Yi , Yong-Jin Liu

The field of video generation has expanded significantly in recent years, with controllable and compositional video generation garnering considerable interest. Most methods rely on leveraging annotations such as text, objects' bounding…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Aram Davtyan , Sepehr Sameni , Björn Ommer , Paolo Favaro

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu