  This paper presents a novel image inpainting framework for face mask removal.
Although current methods have demonstrated their impressive ability in
recovering damaged face images, they suffer from two main problems: the
dependence on manually labeled missing regions and the deterministic result
corresponding to each input. The proposed approach tackles these problems by
integrating a multi-task 3D face reconstruction module with a face inpainting
module. Given a masked face image, the former predicts a 3DMM-based
reconstructed face together with a binary occlusion map, providing dense
geometrical and textural priors that greatly facilitate the inpainting task of
the latter. By gradually controlling the 3D shape parameters, our method
generates high-quality dynamic inpainting results with different expressions
and mouth movements. Qualitative and quantitative experiments verify the
effectiveness of the proposed method.
