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In this work, we propose a Robust, Efficient, and Component-specific makeuptransfer method (abbreviated as BeautyREC). A unique departure from priormethods that leverage global attention, simply concatenate features, orimplicitly manipulate features in latent space, we propose a component-specificcorrespondence to directly transfer the makeup style of a reference image tothe corresponding components (e.g., skin, lips, eyes) of a source image, makingelaborate and accurate local makeup transfer. As an auxiliary, the long-rangevisual dependencies of Transformer are introduced for effective global makeuptransfer. Instead of the commonly used cycle structure that is complex andunstable, we employ a content consistency loss coupled with a content encoderto implement efficient single-path makeup transfer. The key insights of thisstudy are modeling component-specific correspondence for local makeup transfer,capturing long-range dependencies for global makeup transfer, and enablingefficient makeup transfer via a single-path structure. We also contributeBeautyFace, a makeup transfer dataset to supplement existing datasets. Thisdataset contains 3,000 faces, covering more diverse makeup styles, face poses,and races. Each face has annotated parsing map. Extensive experimentsdemonstrate the effectiveness of our method against state-of-the-art methods.Besides, our method is appealing as it is with only 1M parameters,outperforming the state-of-the-art methods (BeautyGAN: 8.43M, PSGAN: 12.62M,SCGAN: 15.30M, CPM: 9.24M, SSAT: 10.48M).

BeautyREC: Robust, Efficient, and Content-preserving Makeup Transfer