Supervised Learning for Makeup Style Transfer

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dc.contributor.authorStrawa, Natalia
dc.contributor.authorSarwas, Grzegorz
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-01T11:17:51Z
dc.date.available2022-09-01T11:17:51Z
dc.date.issued2022
dc.description.abstract-translatedThis paper addresses the problem of using deep learning for makeup style transfer. For solving this problem, we propose a new supervised method. Additionally, we present a technique for creating a synthetic dataset for makeup transfer used to train our model. The obtained results were compared with six popular methods for makeup transfer using three metrics. The tests were carried out on four available data sets. The proposed method, in many respects, is competitive with the methods used in the literature. Thanks to images of faces with generated synthetic makeup, the proposed method learns to better transfer details, and the learning process is significantly accelerated.en
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 199-208.en
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.25
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49595
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectpřenos make-upucs
dc.subjectpřenos stylu obrázkucs
dc.subjectCycleGANcs
dc.subjectGANcs
dc.subjectzpracování obrazucs
dc.subjecthluboké učenícs
dc.subject.translatedmakeup transferen
dc.subject.translatedimage style transferen
dc.subject.translatedCycleGANen
dc.subject.translatedGANen
dc.subject.translatedimage processingen
dc.subject.translateddeep learningen
dc.titleSupervised Learning for Makeup Style Transferen
dc.typeconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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