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Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, we propose Make-Your-Anchor, a novel system necessitating…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Ziyao Huang , Fan Tang , Yong Zhang , Xiaodong Cun , Juan Cao , Jintao Li , Tong-Yee Lee

Recent advancements in video generation have primarily leveraged diffusion models for short-duration content. However, these approaches often fall short in modeling complex narratives and maintaining character consistency over extended…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Canyu Zhao , Mingyu Liu , Wen Wang , Weihua Chen , Fan Wang , Hao Chen , Bo Zhang , Chunhua Shen

Robust perception and dynamics modeling are fundamental to real-world robotic policy learning. Recent methods employ video diffusion models (VDMs) to enhance robotic policies, improving their understanding and modeling of the physical…

Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered. Previous image-based streaming diffusion models have powered efficient and creative live streaming products but have…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Tianrui Feng , Zhi Li , Shuo Yang , Haocheng Xi , Muyang Li , Xiuyu Li , Lvmin Zhang , Keting Yang , Kelly Peng , Song Han , Maneesh Agrawala , Kurt Keutzer , Akio Kodaira , Chenfeng Xu

Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source models available for researchers and engineers. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Haoxin Chen , Menghan Xia , Yingqing He , Yong Zhang , Xiaodong Cun , Shaoshu Yang , Jinbo Xing , Yaofang Liu , Qifeng Chen , Xintao Wang , Chao Weng , Ying Shan

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

Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Pengxiang Li , Kai Chen , Zhili Liu , Ruiyuan Gao , Lanqing Hong , Guo Zhou , Hua Yao , Dit-Yan Yeung , Huchuan Lu , Xu Jia

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Levon Khachatryan , Andranik Movsisyan , Vahram Tadevosyan , Roberto Henschel , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

Recent interactive video world model methods generate scene evolution conditioned on user instructions. Although they achieve impressive results, two key limitations remain. First, they exhibit motion drift in complex environments with…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Guangyuan Li , Bo Li , Jinwei Chen , Xiaobin Hu , Lei Zhao , Peng-Tao Jiang

Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Zheng-Hui Huang , Zhixiang Wang , Jiaming Tan , Ruihan Yu , Yidan Zhang , Bo Zheng , Yu-Lun Liu , Yung-Yu Chuang , Kaipeng Zhang

Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content. This survey provides a comprehensive overview of the critical components of diffusion models for video…

Computer Vision and Pattern Recognition · Computer Science 2024-11-19 Andrew Melnik , Michal Ljubljanac , Cong Lu , Qi Yan , Weiming Ren , Helge Ritter

We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We incorporate three key ingredients: 1) an internet-scale video-action…

Computer Vision and Pattern Recognition · Computer Science 2026-01-07 Loïc Magne , Anas Awadalla , Guanzhi Wang , Yinzhen Xu , Joshua Belofsky , Fengyuan Hu , Joohwan Kim , Ludwig Schmidt , Georgia Gkioxari , Jan Kautz , Yisong Yue , Yejin Choi , Yuke Zhu , Linxi "Jim" Fan

Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior. However, applying these models to video super-resolution remains…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Shangchen Zhou , Peiqing Yang , Jianyi Wang , Yihang Luo , Chen Change Loy

Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Taegyeong Lee , Soyeong Kwon , Taehwan Kim

Generative models trained on internet data have revolutionized how text, image, and video content can be created. Perhaps the next milestone for generative models is to simulate realistic experience in response to actions taken by humans,…

Artificial Intelligence · Computer Science 2024-09-27 Sherry Yang , Yilun Du , Kamyar Ghasemipour , Jonathan Tompson , Leslie Kaelbling , Dale Schuurmans , Pieter Abbeel

Predicting the dynamics of interacting objects is essential for both humans and intelligent systems. However, existing approaches are limited to simplified, toy settings and lack generalizability to complex, real-world environments. Recent…

Computer Vision and Pattern Recognition · Computer Science 2025-04-07 Rick Akkerman , Haiwen Feng , Michael J. Black , Dimitrios Tzionas , Victoria Fernández Abrevaya

Real-time character control is an essential component for interactive experiences, with a broad range of applications, including physics simulations, video games, and virtual reality. The success of diffusion models for image synthesis has…

Computer Vision and Pattern Recognition · Computer Science 2024-08-19 Yi Shi , Jingbo Wang , Xuekun Jiang , Bingkun Lin , Bo Dai , Xue Bin Peng

We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we present InterGen, an effective diffusion-based approach that…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Han Liang , Wenqian Zhang , Wenxuan Li , Jingyi Yu , Lan Xu

We address the problem of generating long-horizon videos for robotic manipulation tasks. Text-to-video diffusion models have made significant progress in photorealism, language understanding, and motion generation but struggle with…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Liudi Yang , Yang Bai , George Eskandar , Fengyi Shen , Mohammad Altillawi , Dong Chen , Soumajit Majumder , Ziyuan Liu , Gitta Kutyniok , Abhinav Valada

Large-scale text-to-video (T2V) diffusion models have great progress in recent years in terms of visual quality, motion and temporal consistency. However, the generation process is still a black box, where all attributes (e.g., appearance,…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Jiwen Yu , Xiaodong Cun , Chenyang Qi , Yong Zhang , Xintao Wang , Ying Shan , Jian Zhang