Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions

Xu Shi, Chuanchen Luo, Junran Peng, Hongwen Zhang, Yunlian Sun

Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions: 7 upvotes on Hugging Face Daily Papers, #25 of 27 papers on 2023-12-06. Day-by-day upvote history.

Recently, significant progress has been made in text-based motion generation, enabling the generation of diverse and high-quality human motions that conform to textual descriptions. However, it remains challenging to generate fine-grained or stylized motions due to the lack of datasets annotated with detailed textual descriptions. By adopting a divide-and-conquer strategy, we propose a new framework named Fine-Grained Human Motion Diffusion Model (FG-MDM) for human motion generation. Specifically, we first parse previous vague textual annotation into fine-grained description of different body parts by leveraging a large language model (GPT-3.5). We then use these fine-grained descriptions to guide a transformer-based diffusion model. FG-MDM can generate fine-grained and stylized motions even outside of the distribution of the training data. Our experimental results demonstrate the superiority of FG-MDM over previous methods, especially the strong generalization capability. We will release our fine-grained textual annotations for HumanML3D and KIT.

Paper page on Hugging Face · arXiv

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