3D human mesh recovery （HMR） has extensive applications in virtual reality， augmented reality， autonomous driving， and sports science. However， the existing methods suffer from imprecise alignment between 3D models and 2D keypoints and often neglect the inherent symmetry and proportion constraints of the human body， resulting in recovered meshes that violate fundamental human characteristics.Our paper proposes a pose-constrained human mesh refinement （PC-HMR） algorithm that optimizes shape and pose parameters through constraint fusion. For shape parameter optimization， we introduce human body symmetry features and skeletal proportion constraints. For pose parameter optimization， we designe a multi-algorithm fusion framework that systematically integrates complementary algorithms to replace traditional single-path selection mechanisms.We evaluate the proposed method in two standard datasets： 3DPW and Human3.6M. Experimental results demonstrate that PC-HMR achieve superior performance compared to existing state-of-the-art methods. Ablation studies further validate the contribution of each module to overall performance.