Harmonious Music-Driven Group Choreography with Trajectory-Controllable Diffusion

Abstract

Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method's superiority.

Cite

Text

Dai et al. "Harmonious Music-Driven Group Choreography with Trajectory-Controllable Diffusion." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I3.32268

Markdown

[Dai et al. "Harmonious Music-Driven Group Choreography with Trajectory-Controllable Diffusion." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/dai2025aaai-harmonious/) doi:10.1609/AAAI.V39I3.32268

BibTeX

@inproceedings{dai2025aaai-harmonious,
  title     = {{Harmonious Music-Driven Group Choreography with Trajectory-Controllable Diffusion}},
  author    = {Dai, Yuqin and Zhu, Wanlu and Li, Ronghui and Ren, Zeping and Zhou, Xiangzheng and Ying, Jixuan and Li, Jun and Yang, Jian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {2645-2653},
  doi       = {10.1609/AAAI.V39I3.32268},
  url       = {https://mlanthology.org/aaai/2025/dai2025aaai-harmonious/}
}