SemGAN: A Semantic and Hierarchical Adversarial Network for 3D Human Pose Estimation

In recent years, significant progress has been made in estimat-ing 3D human joint positions from monocular 2D images or videos. How-ever, existing approaches that lift 2D to 3D often rely on mean per-jointposition error (MPJPE) as explicit supervision, while ignoring implicitsemantic consistency across 2D–3D sequences and disregarding humankinematic constraints. As a result, these methods may produce physio-logically implausible poses. To address this issue, we propose SemGAN,an adversarial learning framework that integrates explicit supervisionwith implicit semantic constraints. Specifically, we employ a two-stageadversarial learning approach that complements explicit supervision withmotion semantic consistency as an implicit constraint, which is furtherenforced through an Entropy-based Cross-Frequency Discriminator(ECF-Discriminator). This design jointly optimizes both joint coordinate ac-curacy and motion semantic consistency. Moreover, guided by humankinematic principles, we construct a Semantic Prototype-guided Hierarchi-cal Generator(SPH-Generator), which enhances local part-level uniformityand global inter-part coordination, thereby alleviating abnormal 3D posegeneration. Extensive experiments on the Human3.6M and MPI-INF-3DHP benchmark datasets demonstrate that our method outperformsprevious state-of-the-art approaches.