  Dance plays an important role as an artistic form and expression in human
culture, yet the creation of dance remains a challenging task. Most dance
generation methods primarily rely solely on music, seldom taking into
consideration intrinsic attributes such as music style or genre. In this work,
we introduce Flexible Dance Generation with Style Description Prompts (DGSDP),
a diffusion-based framework suitable for diversified tasks of dance generation
by fully leveraging the semantics of music style. The core component of this
framework is Music-Conditioned Style-Aware Diffusion (MCSAD), which comprises a
Transformer-based network and a music Style Modulation module. The MCSAD seemly
integrates music conditions and style description prompts into the dance
generation framework, ensuring that generated dances are consistent with the
music content and style. To facilitate flexible dance generation and
accommodate different tasks, a spatial-temporal masking strategy is effectively
applied in the backward diffusion process. The proposed framework successfully
generates realistic dance sequences that are accurately aligned with music for
a variety of tasks such as long-term generation, dance in-betweening, dance
inpainting, and etc. We hope that this work has the potential to inspire dance
generation and creation, with promising applications in entertainment, art, and
education.
