Motion Generation from Fine-grained Textual Descriptions

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.