Dance2Music: Automatic Dance-driven Music Generation
Abstract
Dance and music typically go hand in hand. The complexities in dance, music, and their synchronisation make them fascinating to study from a computational creativity perspective. While several works have looked at generating dance for a given music, automatically generating music for a given dance remains under-explored. This capability could have several creative expression and entertainment applications. We present some early explorations in this direction. We present a search-based offline approach that generates music after processing the entire dance video and an online approach that uses a deep neural network to generate music on-the-fly as the video proceeds. We compare these approaches to a strong heuristic baseline via human studies and present our findings. We have integrated our online approach in a live demo! A video of the demo can be found here: https://sites.google.com/view/dance2music/live-demo.
Cite
@article{arxiv.2107.06252,
title = {Dance2Music: Automatic Dance-driven Music Generation},
author = {Gunjan Aggarwal and Devi Parikh},
journal= {arXiv preprint arXiv:2107.06252},
year = {2021}
}