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Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development

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Modelling and Development of Intelligent Systems (MDIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1761))

Abstract

Creative industries were thought to be the most difficult avenue for Computer Science to enter and to perform well at. Fashion is an integral part of day to day life, one necessary both for displaying style, feelings and conveying artistic emotions, and for simply serving the purely functional purpose of keeping our bodies warm and protected from external factors. The Covid-19 pandemic has accelerated several trends that had been forming in the clothing and textile industry. With the large-scale adoption of Artificial Intelligence (AI) and Deep Learning technologies, the fashion industry is at a turning point. AI is now in charge of supervising the supply chain, manufacturing, delivery, marketing and targeted advertising for clothes and wearable and could soon replace designers too. Clothing design for purely digital environments such as the Metaverse, different games and other on-line specific activities is a niche with a huge potential for market growth. This article wishes to explain the way in which Big Data and Machine Learning are used to solve important issues in the fashion industry in the post-Covid context and to explore the future of clothing and apparel design via artificial generative design. We aim to explore the new opportunities offered to the development of the fashion industry and textile patterns by using of the generative models. The article focuses especially on Generative Adversarial Networks (GAN) but also briefly analyzes other generative models, their advantages and shortcomings. To this regard, we undertook several experiments that highlighted some disadvantages of GANs. Finally, we suggest future research niches and possible hindrances that an end user might face when trying to generate their own fashion models using generative deep learning technologies.

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Notes

  1. 1.

    Nikoleta Kerinska, professor, PhD in art sciences considers that the computer can simulate creativity using AI, but does not have from the beginning an artistic goal, as humans have. https://www.heuritech.com/articles/fashion-solutions/artificial-intelligence-fashion-creativity/.

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Acknowledgement

The first author, Dana Simian, was supported from the project financed by Lucian Blaga University of Sibiu through the research grant LBUS-IRG-2022-08.

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  1. Faculty of Sciences, Research Center in Informatics and Information Technology, Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian & Felix Husac

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  1. Dana Simian
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Correspondence to Dana Simian .

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  1. Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian

  2. Lucian Blaga University of Sibiu, Sibiu, Romania

    Laura Florentina Stoica

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Simian, D., Husac, F. (2023). Challenges and Opportunities in Deep Learning Driven Fashion Design and Textiles Patterns Development. In: Simian, D., Stoica, L.F. (eds) Modelling and Development of Intelligent Systems. MDIS 2022. Communications in Computer and Information Science, vol 1761. Springer, Cham. https://doi.org/10.1007/978-3-031-27034-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-27034-5_12

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