Texture-Shape Optimized GAT for 3D Face Reconstruction
Chen Wang, Chao Hao, Guijin Wang, Nan Su · 2023
3D face reconstruction is widely used in face recognition research, online makeup, etc. However, texture and shape distortion regions usually exist in the reconstruction results. This paper proposes a novel framework named Texture-Shape optimized GAT for 3D face Reconstruction (TSGAT-3D), including data preprocessing and training phases. In the data preprocessing phase, we adopt the styleGAN2 to convert single-view images to multi-view images set. In the training phase, we design a novel Texture-Shape optimized Graph Attention Network, which can learn the facial prior knowledge from the multi-view images set, to improve the details of the initially reconstructed faces based on the auto-encoder module. This network can aggregate the features of face vertices according to the correlation of vertices, thereby improving the accuracy of the 3D reconstructed faces. Furthermore, we present a view loss function for this framework to constrain the shape and texture of the reconstructed face. Extensive experiments conducted on CelebA and Bosphorus show that the reconstruction results of our proposed method are closer to the real 3D faces.