Dance generation, as a branch of human motion generation, has attracted
increasing attention. Recently, a few works attempt to enhance dance
expressiveness, which includes genre matching, beat alignment, and dance
dynamics, from certain aspects. However, the enhancement is quite limited as
they lack comprehensive consideration of the aforementioned three factors. In
this paper, we propose ExpressiveBailando, a novel dance generation method
designed to generate expressive dances, concurrently taking all three factors
into account. Specifically, we mitigate the issue of speed homogenization by
incorporating frequency information into VQ-VAE, thus improving dance dynamics.
Additionally, we integrate music style information by extracting genre- and
beat-related features with a pre-trained music model, hence achieving
improvements in the other two factors. Extensive experimental results
demonstrate that our proposed method can generate dances with high
expressiveness and outperforms existing methods both qualitatively and
quantitatively