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

Plan, Posture and Go: Towards Open-World Text-to-Motion Generation