arXiv cs.AI· Jing Xu, Cunjian Chen, Qiuhong Ke·· 12 小时前AI 评分22
ChoreoStudio：在音乐驱动的群舞编排中规划“跳什么、何时变、谁来动”
ChoreoStudio: Planning What, When, and Who in Music-Driven Group Choreography
AI 导读
ChoreoStudio 将音乐驱动的群舞生成拆成显式编排规划：音乐解耦器分离帧级节奏与片段级结构特征，冻结的全模态模型充当“编剧”生成全局语义描述，再由 Director 输出队形点集、切换门控与移动分配三项耦合决策。
正文 · 原文
Abstract:Music-driven group dance generation must decide not only how each dancer moves, but also what formation the group should form, when it should change, and who should execute each transition. Existing methods can generate orderly and rhythmically synchronized group motion, but remain limited in formation evolution and inter-dancer interaction. In human choreography, formation transitions often align with changes in high-level musical structure, such as the progression from an introduction to a chorus or from a chorus to a climax. We present ChoreoStudio, which makes choreography planning explicit. A music disentangler first decomposes the audio into frame-level rhythmic features and segment-level structural features. Given the segment-level features and sparse labels, a frozen omni-modal model acts as a scriptwriter, producing a global semantic description that guides the Director's choreography planning. Based on this context, the Director produces three coupled interaction decisions: a centroid-relative point set specifies what formation to produce, a transition gate determines when it should change, and a mobility allocation indicates who should execute the transition. The transition gate and mobility allocation are further conditioned on the planned geometric change, coupling formation targets with their temporal and dancer-specific execution. At a finer temporal scale, frame-aligned music features condition the diffusion generator to control detailed body motion and preserve beat-level alignment. The segment-level interaction plan is injected in parallel through frame-aligned instruction tokens and cross-dancer attention biases, coordinating how the generator realizes each transition. Experiments on AIOZ-GDance demonstrate that ChoreoStudio follows the planned interactions while achieving state-of-the-art performance in motion quality, group organization, and formation dynamics.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.32325 [cs.SD] |
| (or arXiv:2610.32325v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32325 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jing Xu [view email]
[v1]
Fri, 23 Oct 2026 08:23:10 UTC (4,824 KB)
来源：arXiv cs.AI · arxiv.org