  We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel
approach for human motion generation from textual descriptions of multiple
actions, utilizing the strengths of discrete diffusion models. This approach
adeptly addresses the challenge of generating multi-motion sequences, ensuring
seamless transitions of motions and coherence across a series of actions. The
strength of M2D2M lies in its dynamic transition probability within the
discrete diffusion model, which adapts transition probabilities based on the
proximity between motion tokens, encouraging mixing between different modes.
Complemented by a two-phase sampling strategy that includes independent and
joint denoising steps, M2D2M effectively generates long-term, smooth, and
contextually coherent human motion sequences, utilizing a model trained for
single-motion generation. Extensive experiments demonstrate that M2D2M
surpasses current state-of-the-art benchmarks for motion generation from text
descriptions, showcasing its efficacy in interpreting language semantics and
generating dynamic, realistic motions.
