An application of generative AI for knitted textile design in fashion

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.

Keywords:

• Fashion
• textile
• creative design process
• generative AI
• deep learning
• generative adversarial network (GAN)
• computational creativity

Acknowledgements

The authors would like to especially thank Ms. Cally Kwong Mei Wan for her continued support of this project.

Ethical approval

The survey questionnaire in this study was conducted in accordance with the approved application for ‘Ethical Review for Teaching/Research Involving Human Subjects’ by the Hong Kong Polytechnic University Institutional Review Board on 25 March 2022 (Ref. # HSEARS20220323006).

Statement of informed consent

Informed consent of the participants was obtained before conducting the survey (via https://forms.gle/6viypo5Xjfm9NuwZA, with the full consent agreement detailed here: https://drive.google.com/file/d/17cxOAkbQ12lpHmxUiTmCQc9fZxCF1iGy/view?usp=sharing), including permission to use their responses towards future research/publications. Any personal details collected from participants are kept strictly confidential by the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the image datasets and survey data that support the findings of this study are available from the authors upon reasonable request.

Additional information

Funding

This work was financially supported by the Hong Kong General Research Fund (Project No. 15602323).

Notes on contributors

Xiaopei Wu

Xiaopei Wu is a PhD graduate and postdoctoral fellow in the School of Fashion & Textiles at The Hong Kong Polytechnic University. Previously, she obtained a BSc in Fiber Science & Apparel Design at Cornell University and has 10 years of professional experience working in the fashion industry, managing knitwear product development and production at global companies. Her current research interests lie in the application of digital tools and methodologies for fashion & textile creative design processes.

Li Li

Li Li is a professor in the School of Fashion & Textiles at The Hong Kong Polytechnic University, an Associate Director of the PolyU Academy for Interdisciplinary Research and is on the Board of Directors of The Hong Kong Research Institute of Textiles and Apparel Limited (HKRITA). Prior to her academic experience, she acquired many years of practical experience as a senior designer and eventually design director in the knitwear fashion industry. Her research focuses on applying concepts of creative economy, design thinking, and interdisciplinary design methods towards the smart functional textile technologies and advanced manufacturing processes.