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PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

Author(s): Chang, Huiwen; Lu, Jingwan; Yu, Fisher; Finkelstein, Adam

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dc.contributor.authorChang, Huiwen-
dc.contributor.authorLu, Jingwan-
dc.contributor.authorYu, Fisher-
dc.contributor.authorFinkelstein, Adam-
dc.identifier.citationChang, Huiwen, Jingwan Lu, Fisher Yu, and Adam Finkelstein. "PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): pp. 40-48. doi:10.1109/CVPR.2018.00012en_US
dc.description.abstractThis paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain transfer problem, our style transfer problem involves two asymmetric functions: a forward function encodes example-based style transfer, whereas a backward function removes the style. We construct two coupled networks to implement these functions - one that transfers makeup style and a second that can remove makeup - such that the output of their successive application to an input photo will match the input. The learned style network can then quickly apply an arbitrary makeup style to an arbitrary photo. We demonstrate the effectiveness on a broad range of portraits and styles.en_US
dc.format.extent40 - 48en_US
dc.relation.ispartofIEEE/CVF Conference on Computer Vision and Pattern Recognitionen_US
dc.rightsAuthor's manuscripten_US
dc.titlePairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeupen_US
dc.typeConference Articleen_US

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