  In this paper, we address the problem of makeup transfer, which aims at
transplanting the makeup from the reference face to the source face while
preserving the identity of the source. Existing makeup transfer methods have
made notable progress in generating realistic makeup faces, but do not perform
well in terms of color fidelity and spatial transformation. To tackle these
issues, we propose a novel Facial Attribute Transformer (FAT) and its variant
Spatial FAT for high-quality makeup transfer. Drawing inspirations from the
Transformer in NLP, FAT is able to model the semantic correspondences and
interactions between the source face and reference face, and then precisely
estimate and transfer the facial attributes. To further facilitate shape
deformation and transformation of facial parts, we also integrate thin plate
splines (TPS) into FAT, thus creating Spatial FAT, which is the first method
that can transfer geometric attributes in addition to color and texture.
Extensive qualitative and quantitative experiments demonstrate the
effectiveness and superiority of our proposed FATs in the following aspects:
(1) ensuring high-fidelity color transfer; (2) allowing for geometric
transformation of facial parts; (3) handling facial variations (such as poses
and shadows) and (4) supporting high-resolution face generation.
