Web-Based Semantic Framework for Enhanced Human Motion Prediction With MSTIA-Net

Yanzheng He, Pengjun Wang, Xiaochun Guan and Han Li
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Yanzheng He: Wenzhou University, China
Pengjun Wang: Wenzhou University, China
Xiaochun Guan: Wenzhou University, China
Han Li: Wenzhou University, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2025, vol. 21, issue 1, 1-17

Abstract: Researchers in human motion prediction have focused on mathematical modeling of the human skeletal structure, often overlooking the spatio-temporal characteristics of human pose sequences. To address this, we propose the multi-scale spatio-temporal information aggregation net (MSTIA-Net), which includes two key modules: the graph convolutional spatio-temporal information aggregation (GCSTIA) module and the windowed discrete cosine transform (WDCT) temporal encoding module. GCSTIA extracts and integrates multi-scale temporal and spatial features of human motion sequences, while WDCT removes high-frequency noise and compresses data. model's efficacy is demonstrated on three datasets: Human3.6, CMU, and 3DPW, achieving performance improvements of 2.4%, 4.1%, and 1.7%, respectively.

Date: 2025
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