A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres

Jinwoo Kim, Heeseok Oh, Seongjean Kim, Hoseok Tong, Sanghoon Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 3490-3500

Abstract


When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres. Therefore, they tend to return certain patterns of the dance genres that they are familiar with. What if artificial intelligence could be used to help choreographers blend dance genres by suggesting various dances, and one that matches their choreographic style? Numerous task-specific variants of autoregressive networks have been developed for dance generation. Yet, a serious limitation remains that all existing algorithms can return repeated patterns for a given initial pose sequence, which may be inferior. To mitigate this issue, we propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dance-genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture. We conduct extensive experiments on AIST++ along with user studies. Compared to the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates outperforming dance sequences qualitatively and quantitatively.

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[bibtex]
@InProceedings{Kim_2022_CVPR, author = {Kim, Jinwoo and Oh, Heeseok and Kim, Seongjean and Tong, Hoseok and Lee, Sanghoon}, title = {A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {3490-3500} }