Deep Facial Non-Rigid Multi-View Stereo

Abstract

We present a method for 3D face reconstruction from multi-view images with different expressions. We formulate this problem from the perspective of non-rigid multi-view stereo (NRMVS). Unlike previous learning-based methods, which often regress the face shape directly, our method optimizes the 3D face shape by explicitly enforcing multi-view appearance consistency, which is known to be effective in recovering shape details according to conventional multi-view stereo methods. Furthermore, by estimating face shape through optimization based on multi-view consistency, our method can potentially have better generalization to unseen data. However, this optimization is challenging since each input image has a different expression. We facilitate it with a CNN network that learns to regularize the non-rigid 3D face according to the input image and preliminary optimization results. Extensive experiments show that our method achieves the state-of-the-art performance on various datasets and generalizes well to in-the-wild data.