DanceComposer: Dance-to-Music Generation Using a Progressive Conditional Music Generator

A wonderful piece of music is the essence and soul of dance, which motivates the study of automatic music generation for dance. To create appropriate music from dance, cross-modal correlations between dance and music such as rhythm and style, should be considered. However, existing dance-to-music methods have difficulties in achieving rhythmic alignment and stylistic matching simultaneously. Additionally, the diversity of generated samples is limited due to the lack of available paired data. To address these issues, we propose DanceComposer, a novel dance-to-music framework, which generates rhythmically and stylistically consistent multi-track music from dance videos. DanceComposer features a Progressive Conditional Music Generator (PCMG) that gradually incorporates rhythm and style constraints, enabling both rhythmic alignment and stylistic matching. To enhance style control, we introduce a Shared Style Module (SSM) that learns cross-modal features as stylistic constraints. This allows the PCMG can be trained on extensive music-only data and diversifies generated pieces. Quantitative and qualitative results show that our method surpasses the state-of-the-art in overall music quality, rhythmic consistency, and stylistic consistency.