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We propose BareSkinNet, a novel method that simultaneously removes makeup andlighting influences from the face image. Our method leverages a 3D morphablemodel and does not require a reference clean face image or a specified lightcondition. By combining the process of 3D face reconstruction, we can easilyobtain 3D geometry and coarse 3D textures. Using this information, we can infernormalized 3D face texture maps (diffuse, normal, roughness, and specular) byan image-translation network. Consequently, reconstructed 3D face textureswithout undesirable information will significantly benefit subsequentprocesses, such as re-lighting or re-makeup. In experiments, we show thatBareSkinNet outperforms state-of-the-art makeup removal methods. In addition,our method is remarkably helpful in removing makeup to generate consistenthigh-fidelity texture maps, which makes it extendable to many realistic facegeneration applications. It can also automatically build graphic assets of facemakeup images before and after with corresponding 3D data. This will assistartists in accelerating their work, such as 3D makeup avatar creation.

BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction