AutoRetouch: Automatic Professional Face Retouching

Alireza Shafaei, James J. Little, Mark Schmidt; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 990-998

Face retouching is one of the most time-consuming steps in professional photography pipelines. The existing automated approaches blindly apply smoothing on the skin, destroying the delicate texture of the face. We present the first automatic face retouching approach that produces high-quality professional-grade results in less than two seconds. Unlike previous work, we show that our method preserves textures and distinctive features while retouching the skin. We demonstrate that our trained models generalize across datasets and are suitable for low-resolution cellphone images. Finally, we release the first large-scale, professionally retouched dataset with our baseline to encourage further work on the presented problem.