Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation

• URL: http://arxiv.org/abs/2502.05534v1
• Date: Sat, 08 Feb 2025 11:38:12 GMT
• Title: Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation
• Authors: Yin Wang, Mu Li, Jiapeng Liu, Zhiying Leng, Frederick W. B. Li, Ziyao Zhang, Xiaohui Liang,
• Abstract summary: We propose a novel framework for fine-grained text-driven human motion generation. Fg-T2M++ consists of: (1) an LLMs semantic parsing module to extract body part descriptions and semantics from text, (2) a hyperbolic text representation module to encode relational information between text units, and (3) a multi-modal fusion module to hierarchically fuse text and motion features.
• Score: 19.094098673523263
• License:
• Abstract: We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed semantic cues regarding body parts, (2) not fully modeling linguistic structures between words to comprehend text comprehensively. To tackle these limitations, we propose a novel fine-grained framework Fg-T2M++ that consists of: (1) an LLMs semantic parsing module to extract body part descriptions and semantics from text, (2) a hyperbolic text representation module to encode relational information between text units by embedding the syntactic dependency graph into hyperbolic space, and (3) a multi-modal fusion module to hierarchically fuse text and motion features. Extensive experiments on HumanML3D and KIT-ML datasets demonstrate that Fg-T2M++ outperforms SOTA methods, validating its ability to accurately generate motions adhering to comprehensive text semantics.

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