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arXiv · 2502.05534

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

Yin Wang·Mu Li·Jiapeng Liu·Zhiying Leng·Frederick W. B. Li·Ziyao Zhang·Xiaohui Liang
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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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Yin Wang, Mu Li, Jiapeng Liu, Zhiying Leng, Frederick W. B. Li, Ziyao Zhang, Xiaohui Liang. 2025-02-08. Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation. https://arxiv.org/abs/2502.05534

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