  Conventional text-to-motion generation methods are usually trained on limited
text-motion pairs, making them hard to generalize to open-world scenarios. Some
works use the CLIP model to align the motion space and the text space, aiming
to enable motion generation from natural language motion descriptions. However,
they are still constrained to generate limited and unrealistic in-place
motions. To address these issues, we present a divide-and-conquer framework
named PRO-Motion, which consists of three modules as motion planner,
posture-diffuser and go-diffuser. The motion planner instructs Large Language
Models (LLMs) to generate a sequence of scripts describing the key postures in
the target motion. Differing from natural languages, the scripts can describe
all possible postures following very simple text templates. This significantly
reduces the complexity of posture-diffuser, which transforms a script to a
posture, paving the way for open-world generation. Finally, go-diffuser,
implemented as another diffusion model, estimates whole-body translations and
rotations for all postures, resulting in realistic motions. Experimental
results have shown the superiority of our method with other counterparts, and
demonstrated its capability of generating diverse and realistic motions from
complex open-world prompts such as "Experiencing a profound sense of joy". The
project page is available at https://moonsliu.github.io/Pro-Motion.
