  Dance serves as a powerful medium for expressing human emotions, but the
lifelike generation of dance is still a considerable challenge. Recently,
diffusion models have showcased remarkable generative abilities across various
domains. They hold promise for human motion generation due to their adaptable
many-to-many nature. Nonetheless, current diffusion-based motion generation
models often create entire motion sequences directly and unidirectionally,
lacking focus on the motion with local and bidirectional enhancement. When
choreographing high-quality dance movements, people need to take into account
not only the musical context but also the nearby music-aligned dance motions.
To authentically capture human behavior, we propose a Bidirectional
Autoregressive Diffusion Model (BADM) for music-to-dance generation, where a
bidirectional encoder is built to enforce that the generated dance is
harmonious in both the forward and backward directions. To make the generated
dance motion smoother, a local information decoder is built for local motion
enhancement. The proposed framework is able to generate new motions based on
the input conditions and nearby motions, which foresees individual motion
slices iteratively and consolidates all predictions. To further refine the
synchronicity between the generated dance and the beat, the beat information is
incorporated as an input to generate better music-aligned dance movements.
Experimental results demonstrate that the proposed model achieves
state-of-the-art performance compared to existing unidirectional approaches on
the prominent benchmark for music-to-dance generation.
