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Human motion modeling is important for many modern graphics applications,which typically require professional skills. In order to remove the skillbarriers for laymen, recent motion generation methods can directly generatehuman motions conditioned on natural languages. However, it remains challengingto achieve diverse and fine-grained motion generation with various text inputs.To address this problem, we propose MotionDiffuse, the first diffusionmodel-based text-driven motion generation framework, which demonstrates severaldesired properties over existing methods. 1) Probabilistic Mapping. Instead ofa deterministic language-motion mapping, MotionDiffuse generates motionsthrough a series of denoising steps in which variations are injected. 2)Realistic Synthesis. MotionDiffuse excels at modeling complicated datadistribution and generating vivid motion sequences. 3) Multi-LevelManipulation. MotionDiffuse responds to fine-grained instructions on bodyparts, and arbitrary-length motion synthesis with time-varied text prompts. Ourexperiments show MotionDiffuse outperforms existing SoTA methods by convincingmargins on text-driven motion generation and action-conditioned motiongeneration. A qualitative analysis further demonstrates MotionDiffuse'scontrollability for comprehensive motion generation. Homepage:https://mingyuan-zhang.github.io/projects/MotionDiffuse.html

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model