SARA: Controllable Makeup Transfer with Spatial Alignment and
  Region-Adaptive Normalization
Xiaojing Zhong Xinyi Huang Zhonghua Wu Guosheng Lin Qingyao Wu
Makeup transfer is a process of transferring the makeup style from a
reference image to the source images, while preserving the source images'
identities. This technique is highly desirable and finds many applications.
However, existing methods lack fine-level control of the makeup style, making
it challenging to achieve high-quality results when dealing with large spatial
misalignments. To address this problem, we propose a novel Spatial Alignment
and Region-Adaptive normalization method (SARA) in this paper. Our method
generates detailed makeup transfer results that can handle large spatial
misalignments and achieve part-specific and shade-controllable makeup transfer.
Specifically, SARA comprises three modules: Firstly, a spatial alignment module
that preserves the spatial context of makeup and provides a target semantic map
for guiding the shape-independent style codes. Secondly, a region-adaptive
normalization module that decouples shape and makeup style using per-region
encoding and normalization, which facilitates the elimination of spatial
misalignments. Lastly, a makeup fusion module blends identity features and
makeup style by injecting learned scale and bias parameters. Experimental
results show that our SARA method outperforms existing methods and achieves
state-of-the-art performance on two public datasets.