The task of text2motion is to generate motion sequences from given textual
descriptions, where a model should explore the interactions between natural
language instructions and human body movements. While most existing works are
confined to coarse-grained motion descriptions (e.g., "A man squats."),
fine-grained ones specifying movements of relevant body parts are barely
explored. Models trained with coarse texts may not be able to learn mappings
from fine-grained motion-related words to motion primitives, resulting in the
failure in generating motions from unseen descriptions. In this paper, we build
a large-scale language-motion dataset with fine-grained textual descriptions,
FineHumanML3D, by feeding GPT-3.5-turbo with delicate prompts. Accordingly, we
design a new text2motion model, FineMotionDiffuse, which makes full use of
fine-grained textual information. Our experiments show that FineMotionDiffuse
trained on FineHumanML3D acquires good results in quantitative evaluation. We
also find this model can better generate spatially/chronologically composite
motions by learning the implicit mappings from simple descriptions to the
corresponding basic motions.

建立一个大规模的细粒度文本 - 运动数据集 FineHumanML3D，并设计一种新的文本生成运动模型 FineMotionDiffuse，以更好地生成空间 / 时间上的综合动作。