Training pseudo-code of the CDGSPAN model.

Accurate 3D skeletal model is fundamental to human pose estimation and body shape reconstruction, as it encodes intricate motion dynamics and spatial configurations. However, generating high-fidelity 3D skeleton samples that adhere to human kinematic constraints remains a significant challenge. To address this problem, the Constrained Dynamic Graph Spatial Perception Adversarial Network (CDGSPAN) is proposed, which is designed to model and synthesize human motion poses with high realism. CDGSPAN leverages dynamic graph-based operations to capture the spatial angular relationships between skeletal joints, while incorporating a constraint-aware regularization mechanism to guide the learning process. This joint modeling enables the network to effectively learn motion priors from real 3D skeletal samples and generate synthetic poses that closely align with biomechanical plausibility. Extensive experiments demonstrate that CDGSPAN achieves superior performance compared to recent adversarial network frameworks in generating sparse 3D skeletal sequences that preserve natural human motion characteristics.