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Generating high-quality human interactions holds significant value for applications like virtual reality and robotics. However, existing methods often fail to preserve unique individual characteristics or fully adhere to textual…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Lipeng Wang , Hongxing Fan , Haohua Chen , Zehuan Huang , Lu Sheng

The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements. While text-to-video generative diffusion models have recently advanced in creating diverse contents, controlling…

Computer Vision and Pattern Recognition · Computer Science 2024-01-04 Yuxin Zhang , Fan Tang , Nisha Huang , Haibin Huang , Chongyang Ma , Weiming Dong , Changsheng Xu

Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Ronghui Li , Youliang Zhang , Yachao Zhang , Yuxiang Zhang , Mingyang Su , Jie Guo , Ziwei Liu , Yebin Liu , Xiu Li

Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-05-27 Nan Jiang , Zhiyuan Zhang , Hongjie Li , Xiaoxuan Ma , Zan Wang , Yixin Chen , Tengyu Liu , Yixin Zhu , Siyuan Huang

We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. The model adeptly captures human motion…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 David Eduardo Moreno-Villamarín , Anna Hilsmann , Peter Eisert

Text-to-motion generation requires not only grounding local actions in language but also seamlessly blending these individual actions to synthesize diverse and realistic global motions. However, existing motion generation methods primarily…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Peng Jin , Hao Li , Zesen Cheng , Kehan Li , Runyi Yu , Chang Liu , Xiangyang Ji , Li Yuan , Jie Chen

We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unlike existing methods that implicitly model interactions using…

Computer Vision and Pattern Recognition · Computer Science 2025-03-18 Ling-An Zeng , Guohong Huang , Yi-Lin Wei , Shengbo Gu , Yu-Ming Tang , Jingke Meng , Wei-Shi Zheng

Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In this paper, we propose a diffusion-based motion synthesis and…

Computer Vision and Pattern Recognition · Computer Science 2023-01-03 Jihoon Kim , Jiseob Kim , Sungjoon Choi

Generating videos of complex human motions such as flips, cartwheels, and martial arts remains challenging for current video diffusion models. Text-only conditioning is temporally ambiguous for fine-grained motion control, while explicit…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Ashkan Taghipour , Morteza Ghahremani , Zinuo Li , Hamid Laga , Farid Boussaid , Mohammed Bennamoun

We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Jihyun Lee , Shunsuke Saito , Giljoo Nam , Minhyuk Sung , Tae-Kyun Kim

Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, integrating spatial constraints, such as pre-defined motion trajectories and obstacles, remains a challenge…

Computer Vision and Pattern Recognition · Computer Science 2023-10-31 Korrawe Karunratanakul , Konpat Preechakul , Supasorn Suwajanakorn , Siyu Tang

While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Yusuf Dalva , Guocheng Gordon Qian , Maya Goldenberg , Tsai-Shien Chen , Kfir Aberman , Sergey Tulyakov , Pinar Yanardag , Kuan-Chieh Jackson Wang

Recent advancements in diffusion models have led to significant improvements in the generation and animation of 4D full-body human-object interactions (HOI). Nevertheless, existing methods primarily focus on SMPL-based motion generation,…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Yukang Cao , Liang Pan , Kai Han , Kwan-Yee K. Wong , Ziwei Liu

In the realm of motion generation, the creation of long-duration, high-quality motion sequences remains a significant challenge. This paper presents our groundbreaking work on "Infinite Motion", a novel approach that leverages long text to…

Computer Vision and Pattern Recognition · Computer Science 2024-07-15 Mengtian Li , Chengshuo Zhai , Shengxiang Yao , Zhifeng Xie , Keyu Chen , Yu-Gang Jiang

Gestures play a key role in human communication. Recent methods for co-speech gesture generation, while managing to generate beat-aligned motions, struggle generating gestures that are semantically aligned with the utterance. Compared to…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Muhammad Hamza Mughal , Rishabh Dabral , Ikhsanul Habibie , Lucia Donatelli , Marc Habermann , Christian Theobalt

Can we synthesize 3D humans interacting with scenes without learning from any 3D human-scene interaction data? We propose GenZI, the first zero-shot approach to generating 3D human-scene interactions. Key to GenZI is our distillation of…

Computer Vision and Pattern Recognition · Computer Science 2023-11-30 Lei Li , Angela Dai

In this work, we present a data-driven framework for generating diverse in-betweening motions for kinematic characters. Our approach injects dynamic conditions and explicit motion controls into the procedure of motion transitions. Notably,…

Graphics · Computer Science 2024-10-02 Yuchen Chu , Zeshi Yang

Recent video generation research has focused heavily on isolated actions, leaving interactive motions-such as hand-face interactions-largely unexamined. These interactions are essential for emerging biometric authentication systems, which…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Yukang Lin , Yan Hong , Zunnan Xu , Xindi Li , Chao Xu , Chuanbiao Song , Ronghui Li , Haoxing Chen , Jun Lan , Huijia Zhu , Weiqiang Wang , Jianfu Zhang , Xiu Li

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Xiangjun Tang , Linjun Wu , He Wang , Yiqian Wu , Bo Hu , Songnan Li , Xu Gong , Yuchen Liao , Qilong Kou , Xiaogang Jin

Consistent human-centric image and video synthesis aims to generate images or videos with new poses while preserving appearance consistency with a given reference image, which is crucial for low-cost visual content creation. Recent advances…

Computer Vision and Pattern Recognition · Computer Science 2024-12-20 Mingdeng Cao , Chong Mou , Ziyang Yuan , Xintao Wang , Zhaoyang Zhang , Ying Shan , Yinqiang Zheng