Self-supervised 3D Face Reconstruction Based on Detailed Face Mask - ZHU Lei, WANG Shanmin, LIU Qingshan (Jisuanji kexue, 2023)
Self-supervised 3D face reconstruction can alleviate the problem of lack of 3D face data,and has therefore become a hot research topic in recent years.Existing self-supervised methods usually focus on using globally supervised signals and do not pay enough attention to the local details of faces.In order to better recover fine-grained 3D faces with vivid details,this paper proposes a fine-grained 3D face reconstruction method based on face part masks,which can reconstruct fine-grained 3D faces without any 3D face annotation.The main idea is to improve the local accuracy of the reconstructed 3D face by giving refinement constraints on the face region through the face part mask and self-supervised constraints on the face part mask on top of the basic loss functions such as 2D image consistency loss,image deep perception loss,etc.Qualitative and quantitative experiments on AFLW2000-3D and MICC Florence datasets demonstrate the effectiveness and superiority of the proposed method.