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

arXiv: https://arxiv.org/abs/2006.11905 · HTML: https://mlsanity.com/paper/2006.11905/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Generative Disco: Text-to-Video Generation for Music Visualization](https://mlsanity.com/paper/2304.08551/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation](https://mlsanity.com/paper/2403.11626/) (2024)
- [Dance Generation by Sound Symbolic Words](https://mlsanity.com/paper/2306.03646/) (2023)

## Abstract (arXiv)

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.