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Makeup transfer is not only to extract the makeup style of the referenceimage, but also to render the makeup style to the semantic correspondingposition of the target image. However, most existing methods focus on theformer and ignore the latter, resulting in a failure to achieve desiredresults. To solve the above problems, we propose a unified SymmetricSemantic-Aware Transformer (SSAT) network, which incorporates semanticcorrespondence learning to realize makeup transfer and removal simultaneously.In SSAT, a novel Symmetric Semantic Corresponding Feature Transfer (SSCFT)module and a weakly supervised semantic loss are proposed to model andfacilitate the establishment of accurate semantic correspondence. In thegeneration process, the extracted makeup features are spatially distorted bySSCFT to achieve semantic alignment with the target image, then the distortedmakeup features are combined with unmodified makeup irrelevant features toproduce the final result. Experiments show that our method obtains morevisually accurate makeup transfer results, and user study in comparison withother state-of-the-art makeup transfer methods reflects the superiority of ourmethod. Besides, we verify the robustness of the proposed method in thedifference of expression and pose, object occlusion scenes, and extend it tovideo makeup transfer. Code will be available athttps://gitee.com/sunzhaoyang0304/ssat-msp.

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal