Yu, ZhijingZhu, ZhongjieGe, DiTu, RenweiBai, YongqiangYang, YuepingWang, YuerChristie, MarcHan, Ping-HsuanLin, Shih-SyunPietroni, NicoSchneider, TeseoTsai, Hsin-RueyWang, Yu-ShuenZhang, Eugene2025-10-072025-10-072025978-3-03868-295-0https://doi.org/10.2312/pg.20251271https://diglib.eg.org/handle/10.2312/pg20251271Hand skeleton-based gesture recognition is a crucial task in human-computer interaction and virtual reality. It aims to achieve precise classification by analyzing the spatio-temporal dynamics of skeleton joints. However, existing methods struggle to effectively model highly entangled spatio-temporal features and fuse heterogeneous Joint, Bone, and Motion (J/B/JM) modalities. These limitations hinder recognition performance. To address these challenges, we propose an Adaptive Spatio-Temporal Network (ASTD-Net) for gesture recognition. Our approach centers on integrated spatio-temporal feature learning and collaborative optimization. First, for spatial feature learning, we design an Adaptive Multi-Subgraph Convolution Module (AMS-GCN) which mitigates spatial coupling interference and enhances structural representation. Subsequently, for temporal feature learning, we introduce a Multi-Scale Dilated Temporal Fusion Module (MD-TFN) that captures multi-granularity temporal patterns, spanning local details to global evolution. This allows for comprehensive modeling of temporal dependencies. Finally, we propose a Self-Supervised Spatio-Temporal Channel Adaptation Module (SSTC-A). Using a temporal discrepancy loss, SSTC-A dynamically optimizes cross-modal dependencies and strengthens alignment between heterogeneous J/B/JM features, enhancing their fusion. On the SHREC'17 and DHG-14/28 datasets, ASTD-Net achieves recognition accuracies of 97.50% and 93.57%, respectively. This performance surpasses current state-of-the-art methods by up to 0.50% and 1.07%. These results verify the effectiveness and superiority of our proposed method.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Activity recognition and understanding; Neural networksComputing methodologies → Activity recognition and understandingNeural networks10.2312/pg.202512719 pages