Dynamic Differencing-Based Hybrid Network for Improved 3D Skeleton-Based Motion Prediction

Background: Three-dimensional skeleton-based human motion prediction is an essential and challenging task for human–machine interactions, aiming to forecast future poses given a history of previous motions. However, existing methods often fail to effectively model dynamic changes and optimize spatial–temporal features. Methods: In this paper, we introduce Dynamic Differencing-based Hybrid Networks (2DHnet), which addresses these issues with two innovations: the Dynamic Differential Dependencies Extractor (2D-DE) for capturing dynamic features like velocity and acceleration, and the Attention-based Spatial–Temporal Dependencies Extractor (AST-DE) for enhancing spatial–temporal correlations. The 2DHnet combines these into a dual-branch network, offering a comprehensive motion representation. Results: Experiments on the Human3.6M and 3DPW datasets show that 2DHnet significantly outperforms existing methods, with average improvements of 4.7% and 26.6% in MPJPE, respectively.