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.

翻译：本文展示了面罩去除新颖的图像油漆框架。 虽然目前的方法已经表明在恢复受损的面罩图像方面表现出令人印象深刻的能力,但它们面临两个主要问题:依赖人工标记的缺失区域,以及每种输入的相应确定结果。拟议方法通过将多任务3D的重建模块与面罩油漆模块结合起来来解决这些问题。使用面罩图像,前者预测3DMM的重建面孔与二进制封闭图一起,提供密集的几何和纹理前缀,大大便利了后者的油漆工作。通过逐步控制3D形状参数,我们的方法产生高质量的动态画结果,有不同的表达和口动。定性和定量实验可以验证拟议方法的有效性。