Exploring Multi-Modal Control in Music-Driven Dance Generation
This work addresses the need for more controllable dance generation for applications in animation and entertainment, though it appears incremental as it builds on existing methods by adding control features.
The paper tackles the problem of insufficient control in music-driven 3D dance generation by proposing a unified framework that supports multi-modal control, including genre, semantic, and spatial control, while maintaining high-quality dance movements, with experimental results showing it outperforms state-of-the-art methods in motion quality and controllability.
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.