Abstract: To build a unified human body model database / face model database, we propose an efficient system using a standard 3D human body mesh / face mesh model to fit the scanning data, so that the different shape models in the database have the same connectivity. It constructs a candidate area for detecting landmarks, and then uses a Markov network to detect the landmarks. As for human face, it employs a regression tree based algorithm detect 2D landmarks in face images captured from different viewing angles. These 2D landmarks are then fused to compute accurate 3D facial landmarks. After that, a similarity transformation is performed to align the standard template and the scanning model. Then the shape and pose of the standard template and the scanning model are roughly registered by using the deformable graph algorithm guided under feature points. Finally, the deformed standard template is fitted by vertex affine transformation based on dense point correspondence. The system is used to fit the human body models in the CAESAR dataset as well as the raw human body and face models. Experimental results show that the system can capture the geometry of the given data accurately.