Recent learning-based approaches, in which models are trained by single-view
images have shown promising results for monocular 3D face reconstruction, but
they suffer from the ill-posed face pose and depth ambiguity issue. In contrast
to previous works that only enforce 2D feature constraints, we propose a
self-supervised training architecture by leveraging the multi-view geometry
consistency, which provides reliable constraints on face pose and depth
estimation. We first propose an occlusion-aware view synthesis method to apply
multi-view geometry consistency to self-supervised learning. Then we design
three novel loss functions for multi-view consistency, including the pixel
consistency loss, the depth consistency loss, and the facial landmark-based
epipolar loss. Our method is accurate and robust, especially under large
variations of expressions, poses, and illumination conditions. Comprehensive
experiments on the face alignment and 3D face reconstruction benchmarks have
demonstrated superiority over state-of-the-art methods. Our code and model are
released in this https URL

本文提出了基于多视角几何一致的自监督训练架构，目标是在面部姿态和深度估计过程中提供可靠的约束。实验结果表明，该方法在面部姿态、表情和光照等方面的变异下都表现出优越性。