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In recent years, virtual makeup applications have become more and morepopular. However, it is still challenging to propose a robust makeup transfermethod in the real-world environment. Current makeup transfer methods mostlywork well on good-conditioned clean makeup images, but transferring makeup thatexhibits shadow and occlusion is not satisfying. To alleviate it, we propose anovel makeup transfer method, called 3D-Aware Shadow and Occlusion Robust GAN(SOGAN). Given the source and the reference faces, we first fit a 3D face modeland then disentangle the faces into shape and texture. In the texture branch,we map the texture to the UV space and design a UV texture generator totransfer the makeup. Since human faces are symmetrical in the UV space, we canconveniently remove the undesired shadow and occlusion from the reference imageby carefully designing a Flip Attention Module (FAM). After obtaining cleanermakeup features from the reference image, a Makeup Transfer Module (MTM) isintroduced to perform accurate makeup transfer. The qualitative andquantitative experiments demonstrate that our SOGAN not only achieves superiorresults in shadow and occlusion situations but also performs well in large poseand expression variations.

SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer