论文标题
带有生成对抗网络的服装设计
Garment Design with Generative Adversarial Networks
论文作者
论文摘要
设计师倾向于遵守特定的心理集合和最初的思想中的沉重情感投资，通常会阻碍他们在设计思维和构想过程中创新的能力。特别是在时尚界，客户需求的多样性，激烈的全球竞争以及缩水时间（又称“快速时尚”）的收缩，进一步加剧了这一挑战的设计师。深层生成模型的最新进展创造了新的可能性，以通过自动生成和/或设计概念编辑来克服设计师的认知障碍。本文探讨了生成对抗网络（GAN）的功能，用于设计概念的自动属性级编辑。具体而言，属性gan（ATTGAN）---一种生成模型被证明成功地属于人脸的编辑---用于自动编辑服装的视觉属性，并在大型时尚数据集中进行了测试。这些实验支持GAN在属性级别的设计概念编辑中的假设潜力，并强调了未来工作中要解决的一些关键局限性和研究问题。
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often hinder their ability to innovate during the design thinking and ideation process. In the fashion industry, in particular, the growing diversity of customers' needs, the intense global competition, and the shrinking time-to-market (a.k.a., "fast fashion") further exacerbate this challenge for designers. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation and/or editing of design concepts. This paper explores the capabilities of generative adversarial networks (GAN) for automated attribute-level editing of design concepts. Specifically, attribute GAN (AttGAN)---a generative model proven successful for attribute editing of human faces---is utilized for automated editing of the visual attributes of garments and tested on a large fashion dataset. The experiments support the hypothesized potentials of GAN for attribute-level editing of design concepts, and underscore several key limitations and research questions to be addressed in future work.
