  Dance is an important human art form, but creating new dances can be
difficult and time-consuming. In this work, we introduce Editable Dance
GEneration (EDGE), a state-of-the-art method for editable dance generation that
is capable of creating realistic, physically-plausible dances while remaining
faithful to the input music. EDGE uses a transformer-based diffusion model
paired with Jukebox, a strong music feature extractor, and confers powerful
editing capabilities well-suited to dance, including joint-wise conditioning,
and in-betweening. We introduce a new metric for physical plausibility, and
evaluate dance quality generated by our method extensively through (1) multiple
quantitative metrics on physical plausibility, beat alignment, and diversity
benchmarks, and more importantly, (2) a large-scale user study, demonstrating a
significant improvement over previous state-of-the-art methods. Qualitative
samples from our model can be found at our website.
