ShanghaiTech University Knowledge Management System
| SMGDiff: Soccer Motion Generation using diffusion probabilistic models | |
| 2024-11-25 | |
| 状态 | 已发表 |
| 摘要 | Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the human player and the ball. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time and user-controllable soccer motions. Our key idea is to integrate real-time character control with a powerful diffusion-based generative model, ensuring high-quality and diverse output motion. In the first stage, we instantly transform coarse user controls into diverse global trajectories of the character. In the second stage, we employ a transformer-based autoregressive diffusion model to generate soccer motions based on trajectory conditioning. We further incorporate a contact guidance module during inference to optimize the contact details for realistic ball-foot interactions. Moreover, we contribute a large-scale soccer motion dataset consisting of over 1.08 million frames of diverse soccer motions. Extensive experiments demonstrate that our SMGDiff significantly outperforms existing methods in terms of motion quality and condition alignment. |
| 语种 | 英语 |
| DOI | arXiv:2411.16216 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:119396014 |
| WOS类目 | Computer Science, Software Engineering |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/471041 |
| 专题 | 信息科学与技术学院_本科生 信息科学与技术学院_PI研究组_虞晶怡组 信息科学与技术学院_PI研究组_汪婧雅组 |
| 通讯作者 | Yang, Hongdi |
| 作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.ByteDance, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Hongdi,Li, Chengyang,Wu, Zhenxuan,et al. SMGDiff: Soccer Motion Generation using diffusion probabilistic models. 2024. |
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