  Recently, significant progress has been made in text-based motion generation,
enabling the generation of diverse and high-quality human motions that conform
to textual descriptions. However, generating motions beyond the distribution of
original datasets remains challenging, i.e., zero-shot generation. By adopting
a divide-and-conquer strategy, we propose a new framework named Fine-Grained
Human Motion Diffusion Model (FG-MDM) for zero-shot human motion generation.
Specifically, we first parse previous vague textual annotations into
fine-grained descriptions of different body parts by leveraging a large
language model. We then use these fine-grained descriptions to guide a
transformer-based diffusion model, which further adopts a design of part
tokens. FG-MDM can generate human motions beyond the scope of original datasets
owing to descriptions that are closer to motion essence. Our experimental
results demonstrate the superiority of FG-MDM over previous methods in
zero-shot settings. We will release our fine-grained textual annotations for
HumanML3D and KIT.
