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.

本文提出了一种基于 Facial Attribute Transformer（FAT）和其空间变体 Spatial FAT 的高质量化妆转移方法，利用 Transformer 模型语义对源面部和参考面部之间的对应关系和相互作用进行建模，进而准确地估计和转移面部属性，同时通过集成 Thin Plate Splines（TPS），Spatial FAT 还可以转移几何属性，而且在颜色保真度和空间变换方面表现优异，经过严格的定性和定量实验验证了所提出方法的有效性和优越性。