Generative Choreography using Deep Learning
Abstract
Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. In this paper we present a system chor-rnn for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is a deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.
Cite
@article{arxiv.1605.06921,
title = {Generative Choreography using Deep Learning},
author = {Luka Crnkovic-Friis and Louise Crnkovic-Friis},
journal= {arXiv preprint arXiv:1605.06921},
year = {2016}
}
Comments
This article will be presented at the 7th International Conference on Computational Creativity, ICCC2016