基于gan的服装设计:样板师是所有你需要设计服装

N. Kato, Hiroyuki Osone, Kotaro Oomori, Chun Wei Ooi, Yoichi Ochiai
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引用次数: 24

摘要

机器学习最近被应用于多个领域,包括时尚。通过生成图像进行服装设计,可以在没有时装设计师的情况下继承设计并获得灵感,但是利用机器学习进行设计师服装创作的研究很少。最先进的作品旨在实现高清晰度输出图像。然而,在服装设计形象的生成中,所生成的形象的质量应该在多大程度上提供给从设计形象中绘制服装图案的制版者,却没有得到深入的研究。因此,在本文中,我们提出了一种基于gan渐进生长(p - gan)的服装图像生成方法,并进行了用户研究,以研究不同的图像质量因素(如epoch和分辨率)是否会影响参与者的置信度得分。我们讨论了所开发方法的结果和可能的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GANs-based Clothes Design: Pattern Maker Is All You Need to Design Clothing
Machine learning have been recently applied to multiple areas, including fashion. Fashion design by generated images makes it possible to inherit design without fashion designer and get inspiration, however, little research has been done on usage of machine learning for creation of designer clothing. The state-of-the-art works aim for high-definition output images. However in fashion design image generation, it has not been thoroughly investigated to what extent the quality of the generated image should be provided to the pattern makers that draw the costume pattern from the design images. Therefore, in this paper we propose a method of generation of clothing images for pattern makers using Progressive Growing of GANs (P-GANs) and conduct a user study to investigate whether the different image quality factors such as epoch and resolution affect the participants' confidence score. We discuss the results and possible applications of the developed method.
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