Exploring Multi-Modal Control in Music-Driven Dance Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911804069773312 |
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| author | Li, Ronghui Dai, Yuqin Zhang, Yachao Li, Jun Yang, Jian Guo, Jie Li, Xiu |
| author_facet | Li, Ronghui Dai, Yuqin Zhang, Yachao Li, Jun Yang, Jian Guo, Jie Li, Xiu |
| contents | Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process. To address these issues, we propose a unified framework capable of generating high-quality dance movements and supporting multi-modal control, including genre control, semantic control, and spatial control. First, we decouple the dance generation network from the dance control network, thereby avoiding the degradation in dance quality when adding additional control information. Second, we design specific control strategies for different control information and integrate them into a unified framework. Experimental results show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and controllability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Multi-Modal Control in Music-Driven Dance Generation Li, Ronghui Dai, Yuqin Zhang, Yachao Li, Jun Yang, Jian Guo, Jie Li, Xiu Sound Computer Vision and Pattern Recognition Audio and Speech Processing Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process. To address these issues, we propose a unified framework capable of generating high-quality dance movements and supporting multi-modal control, including genre control, semantic control, and spatial control. First, we decouple the dance generation network from the dance control network, thereby avoiding the degradation in dance quality when adding additional control information. Second, we design specific control strategies for different control information and integrate them into a unified framework. Experimental results show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and controllability. |
| title | Exploring Multi-Modal Control in Music-Driven Dance Generation |
| topic | Sound Computer Vision and Pattern Recognition Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.01382 |