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Main Authors: Dai, Yuqin, Zhu, Wanlu, Li, Ronghui, Ren, Zeping, Zhou, Xiangzheng, Ying, Jixuan, Li, Jun, Yang, Jian
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2403.06189
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author Dai, Yuqin
Zhu, Wanlu
Li, Ronghui
Ren, Zeping
Zhou, Xiangzheng
Ying, Jixuan
Li, Jun
Yang, Jian
author_facet Dai, Yuqin
Zhu, Wanlu
Li, Ronghui
Ren, Zeping
Zhou, Xiangzheng
Ying, Jixuan
Li, Jun
Yang, Jian
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_06189
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Harmonious Group Choreography with Trajectory-Controllable Diffusion
Dai, Yuqin
Zhu, Wanlu
Li, Ronghui
Ren, Zeping
Zhou, Xiangzheng
Ying, Jixuan
Li, Jun
Yang, Jian
Computer Vision and Pattern Recognition
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.
title Harmonious Group Choreography with Trajectory-Controllable Diffusion
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2403.06189