Feel The Music: Automatically Generating A Dance For An Input Song
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1.
5 papers.
- 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 485
- 2023Generative Disco: Text-to-Video Generation for Music Visualizationcited by 19
- 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decisioncited by 11
- 2024QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generationcited by 4
- 2023Dance Generation by Sound Symbolic Wordscited by 2
- 2024QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation18 Mar 2024
- 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision7 Jul 2023
- 2023Dance Generation by Sound Symbolic Words6 Jun 2023
- 2023Generative Disco: Text-to-Video Generation for Music Visualization17 Apr 2023
- 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++21 Jan 2021