Title:	Supervised Learning for Makeup Style Transfer
Authors:	Strawa, Natalia Sarwas, Grzegorz
Citation:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 199-208.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject
URI:	http://hdl.handle.net/11025/49595
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Keywords:	přenos make-upu;přenos stylu obrázku;CycleGAN;GAN;zpracování obrazu;hluboké učení
Keywords in different language:	makeup transfer;image style transfer;CycleGAN;GAN;image processing;deep learning
Abstract in different language:	This 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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