Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion

Authors

  • Yuqin Dai PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  • Wanlu Zhu PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  • Ronghui Li Shenzhen International Graduate School, Tsinghua University, China
  • Zeping Ren Shenzhen International Graduate School, Tsinghua University, China
  • Xiangzheng Zhou PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  • Jixuan Ying Shenzhen International Graduate School, Tsinghua University, China
  • Jun Li PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
  • Jian Yang PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

DOI:

https://doi.org/10.1609/aaai.v39i3.32268

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.

Published

2025-04-11

How to Cite

Dai, Y., Zhu, W., Li, R., Ren, Z., Zhou, X., Ying, J., … Yang, J. (2025). Harmonious Music-driven Group Choreography with Trajectory-Controllable Diffusion. Proceedings of the AAAI Conference on Artificial Intelligence, 39(3), 2645–2653. https://doi.org/10.1609/aaai.v39i3.32268

Issue

Section

AAAI Technical Track on Computer Vision II