  In recent years, virtual makeup applications have become more and more
popular. However, it is still challenging to propose a robust makeup transfer
method in the real-world environment. Current makeup transfer methods mostly
work well on good-conditioned clean makeup images, but transferring makeup that
exhibits shadow and occlusion is not satisfying. To alleviate it, we propose a
novel 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 model
and 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 to
transfer the makeup. Since human faces are symmetrical in the UV space, we can
conveniently remove the undesired shadow and occlusion from the reference image
by carefully designing a Flip Attention Module (FAM). After obtaining cleaner
makeup features from the reference image, a Makeup Transfer Module (MTM) is
introduced to perform accurate makeup transfer. The qualitative and
quantitative experiments demonstrate that our SOGAN not only achieves superior
results in shadow and occlusion situations but also performs well in large pose
and expression variations.
