Název:	Supervised Learning for Makeup Style Transfer
Autoři:	Strawa, Natalia Sarwas, Grzegorz
Citace zdrojového dokumentu:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 199-208.
Datum vydání:	2022
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	conferenceObject
URI:	http://hdl.handle.net/11025/49595
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Klíčová slova:	přenos make-upu;přenos stylu obrázku;CycleGAN;GAN;zpracování obrazu;hluboké učení
Klíčová slova v dalším jazyce:	makeup transfer;image style transfer;CycleGAN;GAN;image processing;deep learning
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2022: Full Papers Proceedings

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