Name
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. |
Year | Venue | DocType |
|---|---|---|
2020 | ICCC | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Purva Tendulkar | 1 | 0 | 1.01 |
| Abhishek Das | 2 | 433 | 23.54 |
| Aniruddha Kembhavi | 3 | 528 | 31.87 |
| Devi Parikh | 4 | 2929 | 132.01 |