Makeup-Go: Blind Reversion of Portrait Edit

Ying-Cong Chen, Xiaoyong Shen, Jiaya Jia; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4501-4509

Virtual face beautification (or markup) becomes common operations in camera or image processing Apps, which is actually deceiving. In this paper, we propose the task of restoring a portrait image from this process. As the first attempt along this line, we assume unknown global operations on human faces and aim to tackle the two issues of skin smoothing and skin color change. These two tasks, intriguingly, impose very different difficulties to estimate subtle details and major color variation. We propose a Component Regression Network (CRN) and address the limitation of using Euclidean loss in blind reversion. CRN maps the edited portrait images back to the original ones without knowing beautification operation details. Our experiments demonstrate effectiveness of the system for this novel task.