CoheDancers: Enhancing Interactive Group Dance Generation through Music-Driven Coherence Decomposition

Music-Driven Dance Generation seeks to create dance movements synchronized with music, playing a key role in applications like performance and gaming. While solo dance generation has seen progress, group dance generation remains underexplored. Although several methods have been proposed, existing approaches frequently fail to ensure spatial-temporal coherence, resulting in unrealistic and aesthetically unpleasing performances. To tackle the issue, we introduce CoheDancers, a novel framework for Music-Driven Interactive Group Dance Generation. CoheDancers aims to enhance group dance generation coherence by decomposing it into three key aspects: synchronization, naturalness, and fluidity. Correspondingly, we develop a Cycle Consistency based Dance Synchronization strategy to foster music-dance correspondences, an Auto-Regressive-based Exposure Bias Correction strategy to enhance the fluidity of the generated dances, and an Adversarial Training Strategy to augment the naturalness of the group dance output. Collectively, these strategies enable CoheDancers to produce highly coherent group dances with superior quality. Furthermore, to establish better benchmarks for Group Music2Dance, we construct the most diverse and comprehensive open-source dataset to date, I-Dancers, featuring rich dancer interactions, and create comprehensive evaluation metrics. Experimental evaluations on I-Dancers and other extant datasets substantiate that CoheDancers achieves unprecedented state-of-the-art performance. Code is available at https://github.com/XulongT/CoheDancers.