Feel The Music: Automatically Generating A Dance For An Input Song
This addresses the challenge of creative dance generation for applications in entertainment or AI-driven art, though it appears incremental as it builds on existing ideas of structure alignment.
The paper tackles the problem of automatically generating a dance for any input music by encoding heuristics that align dance structure with music structure, resulting in dances rated as more creative and inspiring by human participants compared to baselines.
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.