A Detail Geometry Learning Network for High-Fidelity Face Reconstruction

In this paper, we propose a Detail Geometry Learning Network (DGLN) approach to investigate the problem of self-supervised high-fidelity face reconstruction from monocular images. Unlike existing methods that rely on detail generators to generate "pseudo-details" where most of the reconstructed detail geometries are inconsistent with real faces. Our DGLN can ensure face personalization and also correctly learn more local face details. Specifically, our method includes two stages: the personalization stage and the detailization stage. In the personalization stage, we design a multi-perception interaction module (MPIM) to adaptively calibrate the weighted responses by interacting with information from different receptive fields to extract distinguishable and reliable features. To further enhance the geometric detail information, in the detailization stage, we develop a multi-resolution refinement network module (MrNet) to estimate the refined displacement map with features from different layers and different domains (i.e. coarse displacement images and RGB images). Finally, we design a novel normal smoothing loss that improves the reconstructed details and realisticity. Extensive experiments demonstrate the superiority of our method over previous work.