GroupDancer: Music to Multi-People Dance Synthesis with Style Collaboration

Different people dance in different styles. So when multiple people dance together, the phenomenon of style collaboration occurs: people need to seek common points while reserving differences in various dancing periods. Thus, we introduce a novel Music-driven Group Dance Synthesis task. Compared with single-people dance synthesis explored by most previous works, modeling the style collaboration phenomenon and choreographing for multiple people are more complicated and challenging. Moreover, the lack of sufficient records for conducting multi-people choreography in prior datasets further aggravates this problem. To address these issues, we construct a rich-annotated 3D Multi-Dancer Choreography dataset (MDC) and newly devise a metric SCEU for style collaboration evaluation. To our best knowledge, MDC is the first 3D dance dataset that collects both individual and collaborated music-dance pairs. Based on MDC, we present a novel framework, GroupDancer, consisting of three stages: Dancer Collaboration, Motion Choreography and Motion Transition. The Dancer Collaboration stage determines when and which dancers should collaborate their dancing styles from music. Afterward, the Motion Choreography stage produces a motion sequence for each dancer. Finally, the Motion Transition stage fills the gaps between the motions to achieve fluent and natural group dance. To make GroupDancer trainable from end to end and able to synthesize group dance with style collaboration, we propose mixed training and selective updating strategies. Comprehensive evaluations on the MDC dataset demonstrate that the proposed GroupDancer model can synthesize quite satisfactory group dance synthesis results with style collaboration.