3D Face Reconstruction based on Weakly-Supervised Learning Morphable Face Model

In this paper, we propose a system for 3D face model reconstruction. Earlier studies on reconstruction methods included the software modeling methods or the instrument scanning modeling methods. But both of the above methods require a lot of development resources and time costs. Therefore, we develop a reconstruction system using a weakly supervised approach combining Convolutional Neural Networks (CNN) and 3D Morphable Face Models (3DMM). Given a sufficient number of 2D face images to train and learn the main features of the face, our system is capable of rapidly constructing 3D face models. The proposed method enhances the efficiency of preprocessing and improves the performance of loss function through image depth feature extraction and regression coefficients. Using two datasets for model evaluation and analysis, this study efficiently reconstructs faces without ground-truth labels.