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This work targets a novel text-driven whole-body motion generation task,which takes a given textual description as input and aims at generatinghigh-quality, diverse, and coherent facial expressions, hand gestures, and bodymotions simultaneously. Previous works on text-driven motion generation tasksmainly have two limitations: they ignore the key role of fine-grained hand andface controlling in vivid whole-body motion generation, and lack a goodalignment between text and motion. To address such limitations, we propose aText-aligned whOle-body Motion generATiOn framework, named HumanTOMATO, whichis the first attempt to our knowledge towards applicable holistic motiongeneration in this research area. To tackle this challenging task, our solutionincludes two key designs: (1) a Holistic Hierarchical VQ-VAE (aka H$^2$VQ) anda Hierarchical-GPT for fine-grained body and hand motion reconstruction andgeneration with two structured codebooks; and (2) a pre-trainedtext-motion-alignment model to help generated motion align with the inputtextual description explicitly. Comprehensive experiments verify that our modelhas significant advantages in both the quality of generated motions and theiralignment with text.

HumanTOMATO: Text-aligned Whole-body Motion Generation