Constrained graph dynamic spatial perception adversarial network for human motion generation.

ABSTRACT: 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 skele

SUBMITTER: Li W 

PROVIDER: S-EPMC12768383 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature