Semi-Supervised Monocular 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer

Abstract

Monocular face reconstruction is a challenging task in computer vision, which aims to recover 3D face geometry from a single RGB face image. Recently, deep learning based methods have achieved great improvements on monocular face reconstruction. However, for deep learning-based methods to reach optimal performance, it is paramount to have large-scale training images with ground-truth 3D face geometry, which is generally difficult for human to annotate. To tackle this problem, we propose a semi-supervised monocular reconstruction method, which jointly optimizes a shape-preserved domain-transfer CycleGAN and a shape estimation network. The framework is semi-supervised trained with 3D rendered images with ground-truth shapes and in-the-wild face images without any extra annotation. The CycleGAN network transforms all realistic images to have the rendered style and is end-to-end trained within the overall framework. This is the key difference compared with existing CycleGAN-based learning methods, which just used CycleGAN as a separate training sample generator. Novel landmark consistency loss and edge-aware shape estimation loss are proposed for our two networks to jointly solve the challenging face reconstruction problem. Extensive experiments on public face reconstruction datasets demonstrate the effectiveness of our overall method as well as the individual components.