论文标题
基于3D先验的非确定性面罩去除面具
Non-Deterministic Face Mask Removal Based On 3D Priors
论文作者
论文摘要
本文提出了一个新颖的图像介绍框架，以拆除面罩。尽管当前的方法已经证明了它们在恢复受损的面部图像方面的令人印象深刻的能力，但它们却有两个主要问题：对手动标记的缺失区域的依赖以及与每个输入相对应的确定性结果。提出的方法通过将多任务3D面部重建模块与面部介绍模块集成在一起来解决这些问题。鉴于蒙面的面部图像，前者预测了一个基于3DMM的重建面部以及二进制遮挡图，提供了密集的几何和质地先验，从而极大地促进了后者的介入任务。通过逐渐控制3D形状参数，我们的方法生成具有不同表达式和口感的高质量动态镶嵌结果。定性和定量实验验证了所提出的方法的有效性。
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.
