Feel The Music: Automatically Generating A Dance For An Input Song

TL;DR

This paper introduces a computational method for automatically generating dance movements aligned with any input music, emphasizing creativity and structural coherence, validated through human perception studies.

Contribution

It presents a flexible heuristic-based approach for automatic dance generation that aligns with musical structure and enhances perceived creativity.

Findings

Participants found the generated dances more creative and inspiring.

The approach enables discovery of creative dance movements.

Perception of creativity varies with dance presentation.

Abstract

We present a general computational approach that enables a machine to generate a dance for any input music. We encode intuitive, flexible heuristics for what a 'good' dance is: the structure of the dance should align with the structure of the music. This flexibility allows the agent to discover creative dances. Human studies show that participants find our dances to be more creative and inspiring compared to meaningful baselines. We also evaluate how perception of creativity changes based on different presentations of the dance. Our code is available at https://github.com/purvaten/feel-the-music.