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Recent years have seen significant progress in human image generation, particularly with the advancements in diffusion models. However, existing diffusion methods encounter challenges when producing consistent hand anatomy and the generated…

Computer Vision and Pattern Recognition · Computer Science 2024-05-01 Anton Pelykh , Ozge Mercanoglu Sincan , Richard Bowden

The video generation field has witnessed rapid improvements with the introduction of recent diffusion models. While these models have successfully enhanced appearance quality, they still face challenges in generating coherent and natural…

Computer Vision and Pattern Recognition · Computer Science 2025-04-21 Yaosi Hu , Zhenzhong Chen , Chong Luo

We present a novel approach for generating 360-degree high-quality, spatio-temporally coherent human videos from a single image. Our framework combines the strengths of diffusion transformers for capturing global correlations across…

Computer Vision and Pattern Recognition · Computer Science 2024-09-25 Ruizhi Shao , Youxin Pang , Zerong Zheng , Jingxiang Sun , Yebin Liu

Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse control signals such as text or keyboard input, limiting their utility for embodied…

Computer Vision and Pattern Recognition · Computer Science 2026-02-23 Linxi Xie , Lisong C. Sun , Ashley Neall , Tong Wu , Shengqu Cai , Gordon Wetzstein

Text-to-image generative models can generate high-quality humans, but realism is lost when generating hands. Common artifacts include irregular hand poses, shapes, incorrect numbers of fingers, and physically implausible finger…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Supreeth Narasimhaswamy , Uttaran Bhattacharya , Xiang Chen , Ishita Dasgupta , Saayan Mitra , Minh Hoai

Diffusion-based methods have achieved significant successes in T2I generation, providing realistic images from text prompts. Despite their capabilities, these models face persistent challenges in generating realistic human hands, often…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Taehyeon Eum , Jieun Choi , Tae-Kyun Kim

Audio-driven talking video generation has advanced significantly, but existing methods often depend on video-to-video translation techniques and traditional generative networks like GANs and they typically generate taking heads and…

Computer Vision and Pattern Recognition · Computer Science 2024-09-13 Steven Hogue , Chenxu Zhang , Hamza Daruger , Yapeng Tian , Xiaohu Guo

Egocentric interactive world models are essential for augmented reality and embodied AI, where visual generation must respond to user input with low latency, geometric consistency, and long-term stability. We study egocentric interaction…

Computer Vision and Pattern Recognition · Computer Science 2026-02-16 Yuxi Wang , Wenqi Ouyang , Tianyi Wei , Yi Dong , Zhiqi Shen , Xingang Pan

AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length…

Computer Vision and Pattern Recognition · Computer Science 2023-03-21 Yingqing He , Tianyu Yang , Yong Zhang , Ying Shan , Qifeng Chen

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the…

Computer Vision and Pattern Recognition · Computer Science 2023-02-03 Eyal Molad , Eliahu Horwitz , Dani Valevski , Alex Rav Acha , Yossi Matias , Yael Pritch , Yaniv Leviathan , Yedid Hoshen

Hand motion plays a central role in human interaction, yet modeling realistic 4D hand motion (i.e., 3D hand pose sequences over time) remains challenging. Research in this area is typically divided into two tasks: (1) Estimation approaches…

Computer Vision and Pattern Recognition · Computer Science 2026-02-26 Zhihao Sun , Tong Wu , Ruirui Tu , Daoguo Dong , Zuxuan Wu

While 3D hand reconstruction from monocular images has made significant progress, generating accurate and temporally coherent motion estimates from videos remains challenging, particularly during hand-object interactions. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Yufei Zhang , Zijun Cui , Jeffrey O. Kephart , Qiang Ji

The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Ariel Lapid , Idan Achituve , Lior Bracha , Ethan Fetaya

Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furthermore, generalization to unseen objects is hindered by the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Sammy Christen , Shreyas Hampali , Fadime Sener , Edoardo Remelli , Tomas Hodan , Eric Sauser , Shugao Ma , Bugra Tekin

Current digital human studies focusing on lip-syncing and body movement are no longer sufficient to meet the growing industrial demand, while human video generation techniques that support interacting with real-world environments (e.g.,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Yingying Fan , Quanwei Yang , Kaisiyuan Wang , Hang Zhou , Yingying Li , Haocheng Feng , Errui Ding , Yu Wu , Jingdong Wang

Generative models such as GANs and diffusion models have demonstrated impressive image generation capabilities. Despite these successes, these systems are surprisingly poor at creating images with hands. We propose a novel training…

Computer Vision and Pattern Recognition · Computer Science 2024-01-29 Yue Yang , Atith N Gandhi , Greg Turk

We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yihong Sun , Hao Zhou , Liangzhe Yuan , Jennifer J. Sun , Yandong Li , Xuhui Jia , Hartwig Adam , Bharath Hariharan , Long Zhao , Ting Liu

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

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Yan Zeng , Guoqiang Wei , Jiani Zheng , Jiaxin Zou , Yang Wei , Yuchen Zhang , Hang Li
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