Graph Refinement with Regression Prior for 3D Face Reconstruction

We propose the Graph Refinement with Regression Prior (GRRP) framework for 3D face reconstruction. The GRRP framework is composed of two component networks, namely the 3D Face Regressor and the Graph-Convolutional Refining (GCR) Network. The 3D Face Regressor is made of a face reconstruction pre-trained model, which is not updated during training and offers a pseudo target to guide the training of the GCR. The GCR proposed to design the graph-refining architecture with appropriate graph convolutional settings for enhancing 3D reconstruction accuracy. Apart from the network design, we also highlight the influences of two objective functions, the adaptive vertex loss and surface normal loss, and point out the difficulty of generating 3D faces of local details. The adaptive vertex loss is designed to adaptively optimize the local facial meshes so that the local undesired vertices can be rectified while the global mesh can be kept, and the surface normal loss is designed to eliminate the flying vertices problem of the generated shape surface. The proposed framework is verified through the experiments on the AFLW2000-3D and MICC Florence datasets and compared with contemporary approaches for evaluation.