An application of generative AI for knitted textile design in fashion

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 e...

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Bibliographic Details
Published in:The Design journal Vol. 27; no. 2; pp. 270 - 290
Main Authors: Wu, Xiaopei, Li, Li
Format: Journal Article
Language:English
Published: Oxford Routledge 03.03.2024
Taylor & Francis Ltd
Subjects:
Artificial intelligence
computational creativity
creative design process
Creativity
Deep learning
Fashion
generative adversarial network (GAN)
Generative adversarial networks
generative AI
Generative artificial intelligence
Machine learning
textile
Textile design
Textiles
ISSN:1460-6925, 1756-3062
Online Access:Get full text
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Summary: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.
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ISSN:1460-6925
1756-3062
DOI:10.1080/14606925.2024.2303236