Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning

TL;DR

This paper introduces a novel sequence-to-sequence model with curriculum learning for long-term dance generation from music, effectively capturing music-dance correspondence and reducing error accumulation in long sequences.

Contribution

It proposes a new seq2seq architecture combined with curriculum learning to improve long-term dance synthesis from music, addressing error propagation and style consistency.

Findings

Outperforms existing methods on automatic metrics

Achieves higher human evaluation scores

Effectively models long-term music-dance alignment

Abstract

Dancing to music is one of human's innate abilities since ancient times. In machine learning research, however, synthesizing dance movements from music is a challenging problem. Recently, researchers synthesize human motion sequences through autoregressive models like recurrent neural network (RNN). Such an approach often generates short sequences due to an accumulation of prediction errors that are fed back into the neural network. This problem becomes even more severe in the long motion sequence generation. Besides, the consistency between dance and music in terms of style, rhythm and beat is yet to be taken into account during modeling. In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance. Furthermore, we propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation, which gently changes the training process from a fully guided teacher-forcing scheme using the previous ground-truth movements, towards a less guided autoregressive scheme mostly using the generated movements instead. Extensive experiments show that our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation. We also make a demo video to demonstrate the superior performance of our proposed approach at https://www.youtube.com/watch?v=lmE20MEheZ8.