Ablation test results.

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.精准的三维骨骼模型是人体姿态估计与身形重建的核心基础，因其蕴含复杂精细的运动动力学特性与空间布局信息。然而，生成符合人体运动学约束的高保真三维骨骼样本仍是一项重大挑战。为解决这一问题，本文提出约束动态图空间感知对抗网络（Constrained Dynamic Graph Spatial Perception Adversarial Network，CDGSPAN），该网络旨在对高真实感人体运动姿态进行建模与合成。CDGSPAN采用基于动态图的运算捕捉骨骼关节间的空间角度关联，同时融入约束感知正则化机制以指导模型学习过程。这种联合建模机制使得该网络能够从真实三维骨骼样本中高效学习运动先验知识，并生成高度符合生物力学合理性的合成姿态。大量实验结果表明，在生成保留自然人体运动特征的稀疏三维骨骼序列任务中，CDGSPAN的性能优于当前主流的对抗网络框架。