FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Fan, Ke Tang, Junshu Cao, Weijian Yi, Ran Li, Moran Gong, Jingyu Zhang, Jiangning Wang, Yabiao Wang, Chengjie Ma, Lizhuang |
| author_facet | Fan, Ke Tang, Junshu Cao, Weijian Yi, Ran Li, Moran Gong, Jingyu Zhang, Jiangning Wang, Yabiao Wang, Chengjie Ma, Lizhuang |
| contents | Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Furthermore, a generation module and an interaction module are designed for our FreeMotion framework to decouple the process of conditional motion generation and finally support the number-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis Fan, Ke Tang, Junshu Cao, Weijian Yi, Ran Li, Moran Gong, Jingyu Zhang, Jiangning Wang, Yabiao Wang, Chengjie Ma, Lizhuang Computer Vision and Pattern Recognition Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Furthermore, a generation module and an interaction module are designed for our FreeMotion framework to decouple the process of conditional motion generation and finally support the number-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously. |
| title | FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.15763 |