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.

通过结合语言结构辅助模块和上下文感知渐进推理模块，我们提出了一种细粒度的方法，用于生成支持精确文本描述的高质量、有条件的人体动作序列。实验证明，我们的方法在 HumanML3D 和 KIT 测试集上胜过了基于文本驱动的动作生成方法，并能够根据文本条件生成更好的视觉确认动作。