Multi-Attribute Regression Network for Face Reconstruction

Abstract

In this paper, we propose a multi-attribute regression network (MARN) to investigate the problem of face reconstruction, especially in challenging cases when faces undergo large variations including severe poses, extreme expressions, and partial occlusions in unconstrained environments. The traditional 3DMM parametric regression method does not distinguish the learning of identity, expression, and attitude attributes, resulting in lacking geometric details in the reconstructed face. We propose to learn a face multi-attribute features during 3D face reconstruction from single 2D images. Our MARN enables the network to better extract the feature information of face identity, expression, and pose attributes. We introduce three loss functions to constrain the above three face attributes respectively. At the same time, we carefully design the geometric contour constraint loss function, using the constraints of sparse 2D face landmarks to improve the reconstructed geometric contour information. The experimental results show that our MARN has achieved significant improvements in 3D face reconstruction and face alignment on the AFLW2000-3D and AFLW datasets.