GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
Xinran Liu, Xu Dong, Shenbin Qian, Diptesh Kanojia, Wenwu Wang, Zhenhua Feng

TL;DR
GCDance is a diffusion-based framework that generates genre-specific 3D dance sequences synchronized with music and guided by descriptive text, ensuring controllability and realism.
Contribution
It introduces a novel text-based control mechanism and multi-task optimization to improve genre control and alignment in music-driven dance generation.
Findings
Outperforms state-of-the-art methods on FineDance and AIST++ datasets.
Effectively incorporates genre and music features for coherent dance synthesis.
Balances realism, accuracy, and style through multi-task optimization.
Abstract
Music-driven dance generation is a challenging task as it requires strict adherence to genre-specific choreography while ensuring physically realistic and precisely synchronized dance sequences with the music's beats and rhythm. Although significant progress has been made in music-conditioned dance generation, most existing methods struggle to convey specific stylistic attributes in generated dance. To bridge this gap, we propose a diffusion-based framework for genre-specific 3D full-body dance generation, conditioned on both music and descriptive text. To effectively incorporate genre information, we develop a text-based control mechanism that maps input prompts, either explicit genre labels or free-form descriptive text, into genre-specific control signals, enabling precise and controllable text-guided generation of genre-consistent dance motions. Furthermore, to enhance the alignment…
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