Reimagining Dance: Real-time Music Co-creation between Dancers and AI

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 professional dance performance and improvisational artistic expression across broader populations.