Reimagining Dance: Real-time Music Co-creation between Dancers and AI
Olga Vechtomova, Jeff Bos

TL;DR
This paper introduces a real-time, AI-driven system that allows dancers to influence musical compositions through their movements, fostering a bidirectional creative partnership in dance performances.
Contribution
It presents a novel multi-modal architecture enabling dancers to shape music dynamically, expanding AI's role from choreography generation to active collaboration in dance.
Findings
Emergent communication patterns between movement and audio features
Enhanced improvisational expression through AI collaboration
Potential applications in professional and amateur dance contexts
Abstract
Dance performance traditionally follows a unidirectional relationship where movement responds to music. While AI has advanced in various creative domains, its application in dance has primarily focused on generating choreography from musical input. We present a system that enables dancers to dynamically shape musical environments through their movements. Our multi-modal architecture creates a coherent musical composition by intelligently combining pre-recorded musical clips in response to dance movements, establishing a bidirectional creative partnership where dancers function as both performers and composers. Through correlation analysis of performance data, we demonstrate emergent communication patterns between movement qualities and audio features. This approach reconceptualizes the role of AI in performing arts as a responsive collaborator that expands possibilities for both…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMusic Technology and Sound Studies
