title: Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions
summary: 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_authors._id: 656fe2eee542334e1370a96b
paper_authors.name: Xu Shi
paper_authors._id: 656fe2eee542334e1370a96c
paper_authors.name: Chuanchen Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T12:45:49.362Z
paper_authors._id: 6423d96030b0e4ab36dcd5b7
paper_authors.avatarUrl: /avatars/4e4a489771bcf5fbef1530e179e2e0c5.svg
paper_authors.fullname: Chuanchen Luo
paper_authors.type: user
paper_authors.user: LogicTrainer
paper_authors._id: 656fe2eee542334e1370a96d
paper_authors.name: Junran Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T12:45:57.996Z
paper_authors._id: 62c11a7360edb2dd7765b80d
paper_authors.avatarUrl: /avatars/92db3258d0ba44daee22952f0644cd93.svg
paper_authors.fullname: Junran Peng
paper_authors.type: user
paper_authors.user: JrPeng
paper_authors._id: 656fe2eee542334e1370a96e
paper_authors.name: Hongwen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-06T12:46:05.204Z
paper_authors._id: 638715a1aaee5cd69dc0c401
paper_authors.avatarUrl: /avatars/f5976b3b02fe86191cb7f59c3dae102b.svg
paper_authors.fullname: Hongwen Zhang
paper_authors.type: user
paper_authors.user: HongwenZhang
paper_authors._id: 656fe2eee542334e1370a96f
paper_authors.name: Yunlian Sun
paper_title: Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions
paper_summary: 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_ai_summary: A Fine-Grained Human Motion Diffusion Model (FG-MDM) uses transformed textual descriptions to generate detailed and stylized human motions, demonstrating superior generalization compared to existing methods.
paper_ai_keywords: motion generation large language model GPT-3.5 transformer-based diffusion model fine-grained descriptions HumanML3D KIT generalization capability