SDRNet: Shape Decoupled Regression Network for 3d face Reconstruction

In the field of computer vision, 3D face reconstruction from single-view images is a long-standing and challenging problem. Following the popular 3DMM-based reconstruction framework, recent works show great concerns about exploring discriminative information of identity and expression for shape regression commonly in coupling ways. Actually, identity and expression information may contribute differently in explaining the intrinsic shape of faces, and the former is inferior to the latter in explaining the great facial shape variations caused by extreme expression. In this paper, we propose a Shape Decoupled Regression Network (SDRNet) consisting of identity-focused branch and expression-focused branch with focused criteria for representation learning, which interact with the union branch to achieve the final 3DMM parameters regression for improved shape reconstruction. In SDRNet, the focused criteria estimate the 3D vertex prediction loss, while the predicted 3D shape is reconstructed only using the predicted parameter of identity or expression and introducing the ground-truth parameters of the left two. Extensive experiments on the challenging AFLW2000-3D and AFLW datasets demonstrate advanced performance in 3D face reconstruction and face alignment.