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FashionGAN: Display your fashion design using Conditional Generative Adversarial Nets
Author(s) -
Cui Y. R.,
Liu Q.,
Gao C. Y.,
Su Z.
Publication year - 2018
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13552
Subject(s) - sketch , computer science , fashion design , generative grammar , image (mathematics) , reuse , artificial intelligence , clothing , computer vision , sample (material) , adversarial system , computer graphics (images) , engineering , algorithm , chemistry , archaeology , chromatography , history , waste management
Abstract Virtual garment display plays an important role in fashion design for it can directly show the design effect of the garment without having to make a sample garment like traditional clothing industry. In this paper, we propose an end‐to‐end virtual garment display method based on Conditional Generative Adversarial Networks. Different from existing 3D virtual garment methods which need complex interactions and domain‐specific user knowledge, our method only need users to input a desired fashion sketch and a specified fabric image then the image of the virtual garment whose shape and texture are consistent with the input fashion sketch and fabric image can be shown out quickly and automatically. Moreover, it can also be extended to contour images and garment images, which further improves the reuse rate of fashion design. Compared with the existing image‐to‐image methods, the quality of images generated by our method is better in terms of color and shape.