Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion
  Model
Yin Wang Zhiying Leng Frederick W. B. Li Shun-Cheng Wu Xiaohui Liang
Text-driven human motion generation in computer vision is both significant
and challenging. However, current methods are limited to producing either
deterministic or imprecise motion sequences, failing to effectively control the
temporal and spatial relationships required to conform to a given text
description. In this work, we propose a fine-grained method for generating
high-quality, conditional human motion sequences supporting precise text
description. Our approach consists of two key components: 1) a
linguistics-structure assisted module that constructs accurate and complete
language feature to fully utilize text information; and 2) a context-aware
progressive reasoning module that learns neighborhood and overall semantic
linguistics features from shallow and deep graph neural networks to achieve a
multi-step inference. Experiments show that our approach outperforms
text-driven motion generation methods on HumanML3D and KIT test sets and
generates better visually confirmed motion to the text conditions.