生成语法
生成设计
织物
服装设计
过程(计算)
创造力
计算机科学
人工智能
生成模型
纺织品设计
深度学习
组分(热力学)
轮廓
质量(理念)
人机交互
工程类
服装
心理学
运营管理
视觉艺术
公制(单位)
考古
历史
社会心理学
艺术
热力学
物理
哲学
认识论
操作系统
出处
期刊:Design Journal
[Informa]
日期:2024-02-02
卷期号:27 (2): 270-290
被引量:13
标识
DOI:10.1080/14606925.2024.2303236
摘要
In recent years, artificial intelligence (AI) in the form of generative deep learning models have proliferated as a tool to facilitate or exhibit creativity across various design fields. When it comes to fashion design, existing applications of AI have more heavily addressed general fashion design elements, such as style, silhouette, colour, and pattern, and paid less attention to the underlying textile attributes. To address this gap, this study explores the effects of applying a generative deep learning model specifically towards the textile component of the fashion design process, by utilizing a Generative Adversarial Network (GAN) model to generate new images of knitted textile designs, which were then assessed based on their aesthetic quality in a qualitative survey with over 200 respondents. The results suggest that the generative deep learning (GAN) based method has the ability to produce new textile designs with creative qualities and practical utility that facilitate the fashion design process. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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