分治时空依赖，多子图增强人体动作预测
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction
- 拆解时空依赖建模，通过一致性约束实现跨域知识迁移
- 采用多子图结构，提升对复杂动作模式的表征能力
- 在标准数据集上表现更优，适合高精度动作预测场景
人体动作预测（HMP）旨在基于历史数据预测未来动作。图卷积网络（GCNs）因能有效捕捉关节间关系而在该领域备受关注。然而，现有基于GCN的方法通常只侧重时序或空间特征，或虽融合两者却未充分挖掘其互补性与交叉依赖。本文提出时空多子图图卷积网络（STMS-GCN），以捕捉人体动作中的复杂时空依赖。具体而言，我们解耦时序与空间依赖建模，通过时空信息一致性约束机制实现多尺度跨域知识迁移；同时，利用多个子图提取更丰富的运动信息，并通过同质信息约束机制增强不同子图间的关联学习。在标准HMP基准上的大量实验表明，本方法具有显著优势。
原文摘要 · Abstract (English)
Human motion prediction (HMP) involves forecasting future human motion based on historical data. Graph Convolutional Networks (GCNs) have garnered widespread attention in this field for their proficiency in capturing relationships among joints in human motion. However, existing GCN-based methods tend to focus on either temporal-domain or spatial-domain features, or they combine spatio-temporal features without fully leveraging the complementarity and cross-dependency of these two features. In this paper, we propose the Spatial-Temporal Multi-Subgraph Graph Convolutional Network (STMS-GCN) to capture complex spatio-temporal dependencies in human motion. Specifically, we decouple the modeling of temporal and spatial dependencies, enabling cross-domain knowledge transfer at multiple scales through a spatio-temporal information consistency constraint mechanism. Besides, we utilize multiple subgraphs to extract richer motion information and enhance the learning associations of diverse subgraphs through a homogeneous information constraint mechanism. Extensive experiments on the standard HMP benchmarks demonstrate the superiority of our method.
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