  The task of text2motion is to generate human motion sequences from given
textual descriptions, where the model explores diverse mappings from natural
language instructions to human body movements. While most existing works are
confined to coarse-grained motion descriptions, e.g., "A man squats.",
fine-grained descriptions specifying movements of relevant body parts are
barely explored. Models trained with coarse-grained texts may not be able to
learn mappings from fine-grained motion-related words to motion primitives,
resulting in the failure to generate motions from unseen descriptions. In this
paper, we build a large-scale language-motion dataset specializing in
fine-grained textual descriptions, FineHumanML3D, by feeding GPT-3.5-turbo with
step-by-step instructions with pseudo-code compulsory checks. Accordingly, we
design a new text2motion model, FineMotionDiffuse, making full use of
fine-grained textual information. Our quantitative evaluation shows that
FineMotionDiffuse trained on FineHumanML3D improves FID by a large margin of
0.38, compared with competitive baselines. According to the qualitative
evaluation and case study, our model outperforms MotionDiffuse in generating
spatially or chronologically composite motions, by learning the implicit
mappings from fine-grained descriptions to the corresponding basic motions. We
release our data at https://github.com/KunhangL/finemotiondiffuse.
