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.
@article{arxiv.2502.05534,
title = {Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation},
author = {Yin Wang and Mu Li and Jiapeng Liu and Zhiying Leng and Frederick W. B. Li and Ziyao Zhang and Xiaohui Liang},
journal= {arXiv preprint arXiv:2502.05534},
year = {2025}
}