Occlusions are very common in face images in the wild, leading to the
degraded performance of face-related tasks. Although much effort has been
devoted to removing occlusions from face images, the varying shapes and
textures of occlusions still challenge the robustness of current methods. As a
result, current methods either rely on manual occlusion masks or only apply to
specific occlusions. This paper proposes a novel face de-occlusion model based
on face segmentation and 3D face reconstruction, which automatically removes
all kinds of face occlusions with even blurred boundaries,e.g., hairs. The
proposed model consists of a 3D face reconstruction module, a face segmentation
module, and an image generation module. With the face prior and the occlusion
mask predicted by the first two, respectively, the image generation module can
faithfully recover the missing facial textures. To supervise the training, we
further build a large occlusion dataset, with both manually labeled and
synthetic occlusions. Qualitative and quantitative results demonstrate the
effectiveness and robustness of the proposed method.

本研究提出了一种基于人脸分割和三维人脸重建的面部去遮挡模型，通过预测面部先验和遮挡掩码实现了对所有种类的面部遮挡（包括毛发等较模糊的边界）的自动去除，并为了监督训练进一步构建了一个大型遮挡数据集。实验证明，该方法有效且鲁棒。