Hierarchical Text Semantics-Driven Multi-Granularity Human Motion Generation

SHU Xiangbo; LI Chengjian; YIN Zheng; LI Pengpeng; LI Zechao; TANG Jinhui

doi:10.12263/DZXB.20251089

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Hierarchical Text Semantics-Driven Multi-Granularity Human Motion Generation

PAPERS | 更新时间：2026-06-22

- Hierarchical Text Semantics-Driven Multi-Granularity Human Motion Generation
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 451-465(2026)
- 作者机构：
  
  1.南京理工大学计算机科学与工程学院，江苏南京 210094
  2.南京林业大学信息科学与技术学院&人工智能学院，江苏南京 210037
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China Major Research Instrument Development Program(62427808);National Natural Science Foundation of China Regional Innovation Development Joint Fund(U25A20442);National Natural Science Foundation of China Excellent Young Scientists Fund(62222207);National Science Fund for Distinguished Young Scholars(62425603)
- DOI：10.12263/DZXB.20251089
  CLC： TP391;
- Received：13 December 2025，
  
  Accepted：06 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
舒祥波, 李成建, 尹政, 等. 层次化文本语义驱动的多粒度人体行为生成[J]. 电子学报, 2026, 54(01): 451-465.

SHU Xiangbo, LI Chengjian, YIN Zheng, et al. Hierarchical Text Semantics-Driven Multi-Granularity Human Motion Generation[J]. Acta Electronica Sinica, 2026, 54(01): 451-465.
舒祥波, 李成建, 尹政, 等. 层次化文本语义驱动的多粒度人体行为生成[J]. 电子学报, 2026, 54(01): 451-465. DOI：10.12263/DZXB.20251089

SHU Xiangbo, LI Chengjian, YIN Zheng, et al. Hierarchical Text Semantics-Driven Multi-Granularity Human Motion Generation[J]. Acta Electronica Sinica, 2026, 54(01): 451-465. DOI：10.12263/DZXB.20251089

摘要

当前的人体行为生成方法在生成文本描述与行为一致的高质量运动方面仍面临挑战。尽管近年来基于扩散模型、自回归模型以及多模态预训练模型的方法在运动自然性和多样性上取得了一定进展，但在复杂文本语义理解和精细动作建模方面仍存在明显不足。其主要原因包括：（1）缺乏句子成分间层次依赖关系建模会导致模型文本语义理解困难；（2）现有方法仅在全局级或单词级进行文本-行为之间跨模态对齐，忽视了全局与局部信息之间的互补性导致粗细粒度协同建模困难。为此，本文提出了一种层次化文本语义驱动的多粒度人体行为生成框架（Hierarchical Textual-semantic-driven Multi-Granularity human motion generation framework，HTMG），该框架在全面理解文本语义的同时实现了粗细粒度的跨模态交互，从而实现文本-行为的一致性。具体而言，为了解决文本语义理解难题，本文提出了一种层次化语义捕捉策略（Hierarchical Semantic Capture Strategy，HSCS），该策略通过句法分析构建文本结构树显式建模单词间依存关系并引入双曲图注意力机制（Hyperbolic Graph ATtention mechanism，HGAT）在双曲空间动态捕捉层次语义依赖，从而显著提升模型的语义理解能力。此外，为了实现粗细粒度的跨模态对齐，本文设计一种多粒度跨模态注意力机制（Multi-Granularity Cross-modal Attention mechanism，MGCA），通过将全局级语义表示与单词级局部语义表示分别与人体行为特征进行自适应交叉融合，使模型在生成过程中能够同时关注整体动作意图与局部动作变化，从而实现语义一致的多粒度动作建模。大量实验结果表明，本文提出的HTMG在HumanML3D和KIT-ML数据集上均取得了最优性能，充分验证了该框架在文本语义理解与文本-行为一致性建模方面的有效性。

Abstract

Generating high-quality human motions that are semantically consistent with textual descriptions remains a challenging problem. Although recent diffusion-based

autoregressive

and multimodal pre-trained approaches have improved motion naturalness and diversity

they still struggle with complex semantic understanding and fine-grained motion modeling. These limitations mainly stem from two factors: (1) the lack of explicit modeling of hierarchical dependency relationships among sentence components

which hampers accurate textual semantic understanding; (2) the reliance on either global-level or word-level text-motion alignment

while neglecting the complementarity between global and local semantics

making coarse-to-fine collaborative modeling difficult. To address these limits

we propose the hierarchical textual-semantic-driven multi-granularity human motion generation framework (HTMG)

which models textual semantics while enabling coarse-to-fine cross-modal interactions to ensure text-motion consistency. Specifically

we introduce a hierarchical semantic capture strategy (HSCS) that constructs a textual structure tree via syntactic parsing and embeds it into hyperbolic space

where hierarchical semantic dependencies are dynamically modeled using a hyperbolic graph attention mechanism. Furthermore

we design a multi-granularity cross-modal attention mechanism (MGCA) that adaptively fuses global-level and word-level semantic representations with motion features

allowing the model to jointly capture overall motion intent and fine-grained action variations. Extensive experiments demonstrate that HTMG achieves state-of-the-art performance on the HumanML3D and KIT-ML benchmarks

validating the effectiveness of our framework in textual semantic understanding and text-motion alignment.

关键词

Keywords

references

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