It is well known that regressing the parametric representation of a human body from a single image suffers low accuracy due to sparse information use and error accumulation. Although being able to achieve higher accuracy by avoiding these issues, directly regressing vertices may result in vertex outliers and can only deal with the body mesh with very limited number of vertices. We present METRO-X, a novel method for reconstructing full-body human meshes with body pose, facial expression and hand gesture from a single image, which combines the advantages from the two disciplines so as to achieve higher accuracy than parameters regression while bear denser vertices and generate smoother shape than vertices regression. It first detects and extracts hands, head and the whole body parts from a given image, then regresses the vertices of three parts separately using METRO, and finally fits SMPL-X to the reconstructed meshes to obtain the complete parametric representation of the human body, facial expression and hand gesture. Experimental results show that METRO-X outperforms the ExPose method, with a significant 23% improvement in body accuracy and a 35% improvement in gesture accuracy. These results demonstrate the potential of our approach in enabling various applications.