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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Vydáno v:	The Design journal Ročník 27; číslo 2; s. 270 - 290
Hlavní autoři:	Wu, Xiaopei, Li, Li
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Routledge 03.03.2024 Taylor & Francis Ltd
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Wu, Xiaopei Li, Li
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SubjectTerms	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
Title	An application of generative AI for knitted textile design in fashion
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