  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.
