Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions
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.
656fe2eee542334e1370a96b
Xu Shi
656fe2eee542334e1370a96c
Chuanchen Luo
admin_assigned
2023-12-06T12:45:49.362Z
6423d96030b0e4ab36dcd5b7
/avatars/4e4a489771bcf5fbef1530e179e2e0c5.svg
Chuanchen Luo
user
LogicTrainer
656fe2eee542334e1370a96d
Junran Peng
admin_assigned
2023-12-06T12:45:57.996Z
62c11a7360edb2dd7765b80d
/avatars/92db3258d0ba44daee22952f0644cd93.svg
Junran Peng
user
JrPeng
656fe2eee542334e1370a96e
Hongwen Zhang
admin_assigned
2023-12-06T12:46:05.204Z
638715a1aaee5cd69dc0c401
/avatars/f5976b3b02fe86191cb7f59c3dae102b.svg
Hongwen Zhang
user
HongwenZhang
656fe2eee542334e1370a96f
Yunlian Sun
Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions
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.
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.
motion generation large language model GPT-3.5 transformer-based diffusion model fine-grained descriptions HumanML3D KIT generalization capability