Review
BibTex RIS Cite

Generative Artificial Intelligence Powered Systems in Fashion Design Process

Year 2024, Volume: 5 Issue: 8, 110 - 122, 28.06.2024

Abstract

Researchers have moved data processing methods from traditional bases to artificial intelligence-based systems, due to the increasing volume of data with advancing technology and digitalization. Through these technologies, experts can achieve faster and more precise results in data analysis, making the scientific discovery process more efficient. Fashion, with its vast ecosystem, attracts the attention of researchers due to its ability to achieve rapid and more effective results in studies such as analyzing complex data, pattern recognition, predictability, and productivity enhancement. The aim of this research is to reveal current applications realized through generative artificial intelligence-supported systems within the scope of design processes in the fashion ecosystem and to discuss the uses of these systems in the fashion industry. This study is a literature based on thematic review analysis. In this context, trend forecasting, market analysis, creativity, modeling, textile, and material applications carried out in the fashion design process are discussed. Current studies that adopt deep learning and generative artificial intelligence-based methods in these areas are investigated and system constructs are presented. In addition, popular examples of real-world applications of the systems are included.

References

  • Albon, C. (2018). Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning. O'Reilly Media Inc., USA.
  • Cao, W. Chai, S. Hao, Y. Zhang, H. Chen & Wang, G. (2023). DiffFashion: Reference-Based Fashion Design With Structure-Aware Transfer by Diffusion Models, IEEE Transactions on Multimedia, 26, 3962-3975. doi: 10.1109/TMM.2023.3318297.
  • Chen, J., Zhao, Y., Zhong, S. & Hu, X. (2023). Fashion Trend Forecasting Based on Multivariate Attention Fusion. In International Conference on Neural Information Processing, 68-81, Springer, Singapore. doi: 10.1007/978-981-99-8132-8_6.
  • Clarisse, I. (2023). Application of Deep Learning Hierarchical Perception Technology in 3D Fashion Design. In International Conference on Frontier Computing 1469–1474, Springer, Japan. doi: 10.1007/978-981-99-1428-9_192.
  • Erlhoff, M. & Marshall, T. (Eds.) (2008). Design dictionary: perspectives on design terminology. Birkhäuser, Germany.
  • Grose, V. (2011). Basics Fashion Management 01: Fashion Merchandising. Bloomsbury Publishing, London. Gu, X., Gao, F., Tan, M., & Peng, P. (2020). Fashion analysis and understanding with artificial intelligence. Information Processing & Management, 57(5), 102-276.
  • Hong, Y., Zeng, X., Brunixaux, P. & Chen, Y. (2018). Evaluation of fashion design using artificial intelligence tools. In: Thomassey, S., Zeng, X. (eds) Artificial Intelligence for Fashion Industry in the Big Data Era, 245-256. Springer Series in Fashion Business. Springer, Singapore. doi: 10.1007/978-981-13-0080-6_12.
  • Luce, L. (2018). Artificial Intelligence For Fashion: How Ai is Revolutionizing the Fashion Industry. Apress, USA. Mueller, J. P. & Massaron, L. (2018). Artificial intelligence for dummies. John Wiley & Sons, Canada.
  • Özalp, E. (2020). Gençlerle Baş Başa Yapay Zekâ. İnkılap Kitapevi, İstanbul.
  • Özkan, İ. & Ülker, E. (2017). Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
  • Soomin, L. & Juhee, P. (2023). A Study on the Improvement of Fabric Property for Virtual Sample Using 3D Virtual Fashion CAD. Journal of the Korean Society of Costume, 73(1), 53-74.
  • Wang, Z., Tao, X., Zeng, X., Xing, Y., Xu, Z. & Bruniaux, P. (2023). Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions. International Journal of Computational Intelligence Systems, 16(16), 1-20. doi: 10.1007/s44196-023-00189-7.
  • Xinrong, H., Lei, C., Ruiqi, L., Junping, L., Jinxing, L., Tao, P, Li, L. & Hang, L. (2022). A Sketch-to-Clothing Image Generation Method Based on StyleGAN, Computer Science and Application, 12(10), 2405-2415. DOI: 10.12677/CSA.2022.1210246.
  • Yoshikawa, T., Endo, Y. & Kanamori, Y. (2023). StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human. In International Conference on Computer Vision Theory and Applications, 1-12, Cornell University, New York.
  • İnternet Kaynakları Url-1. Bilim ve Teknik (2023). Yapay Zekâ. https://bilimteknik.tubitak.gov.tr/system/files/makale/yapayz.pdf, (Erişim tarihi: 25.10.2023).
  • Url 2. GenAi (2023). TRAI üretken yapay zekâ raporu https://turkiye.ai/wp content/uploads/2023/11/02 Gen AI Raporu_Ekim 2023.pdf, (Erişim tarihi: 10.12.2023).
  • Url 3. 9to5case (2023). https://www.9to5case.com/2022/07/fashioning future how materials science.html, (E-rişim tarihi: 10.12.2023).
  • Url-4. Textura (2024). https://textura.ai/product/,( Erişim tarihi: 05.03.2024).

Moda Tasarımı Sürecinde Üretken Yapay Zekâ Destekli Sistemler

Year 2024, Volume: 5 Issue: 8, 110 - 122, 28.06.2024

Abstract

Araştırmacılar, gelişen teknoloji ve dijitalleşmeyle birlikte artan veri hacmi sayesinde, veri işleme yöntemlerini geleneksel temellerden yapay zekâ temelli sistemlere taşımıştır. Bu teknolojiler sayesinde uzmanlar, veri analizinde daha hızlı ve daha hassas sonuçlara ulaşarak, bilimsel keşif süreçlerini verimli hale getirebilmektedirler. Geniş ekosistemiyle moda alanı, karmaşık verinin analizi, desen tanıma, öngörülebilirlik ve üretkenlik artırma gibi çalışmalarda seri ve daha etkili sonuçlar elde edilebilmesi nedeniyle araştırmacıların dikkatini çekmektedir. Bu araştırmanın amacı, moda ekosistemindeki tasarım süreçleri kapsamında, üretken yapay zekâ destekli sistemler aracılığıyla gerçekleştirilen güncel uygulamaları ortaya koymak ve bu sistemlerin moda sektöründeki kullanımlarını tartışmaktır. Çalışma literatür temelli, tematik derlemeye dayalı analiz çalışmasıdır. Bu kapsamda, moda tasarımı sürecinde yürütülen, trend tahmini, pazar analizi, yaratıcılık, modelleme, tekstil ve malzeme uygulamaları ele alınmıştır. Bu alanlarda derin öğrenme ve üretken yapay zekâ tabanlı yöntemleri benimseyen güncel çalışmalar araştırılarak sistem kurguları sunulmuştur. Ayrıca sistemlerin gerçek dünya uygulamalarından popüler örneklere yer verilmiştir.

References

  • Albon, C. (2018). Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning. O'Reilly Media Inc., USA.
  • Cao, W. Chai, S. Hao, Y. Zhang, H. Chen & Wang, G. (2023). DiffFashion: Reference-Based Fashion Design With Structure-Aware Transfer by Diffusion Models, IEEE Transactions on Multimedia, 26, 3962-3975. doi: 10.1109/TMM.2023.3318297.
  • Chen, J., Zhao, Y., Zhong, S. & Hu, X. (2023). Fashion Trend Forecasting Based on Multivariate Attention Fusion. In International Conference on Neural Information Processing, 68-81, Springer, Singapore. doi: 10.1007/978-981-99-8132-8_6.
  • Clarisse, I. (2023). Application of Deep Learning Hierarchical Perception Technology in 3D Fashion Design. In International Conference on Frontier Computing 1469–1474, Springer, Japan. doi: 10.1007/978-981-99-1428-9_192.
  • Erlhoff, M. & Marshall, T. (Eds.) (2008). Design dictionary: perspectives on design terminology. Birkhäuser, Germany.
  • Grose, V. (2011). Basics Fashion Management 01: Fashion Merchandising. Bloomsbury Publishing, London. Gu, X., Gao, F., Tan, M., & Peng, P. (2020). Fashion analysis and understanding with artificial intelligence. Information Processing & Management, 57(5), 102-276.
  • Hong, Y., Zeng, X., Brunixaux, P. & Chen, Y. (2018). Evaluation of fashion design using artificial intelligence tools. In: Thomassey, S., Zeng, X. (eds) Artificial Intelligence for Fashion Industry in the Big Data Era, 245-256. Springer Series in Fashion Business. Springer, Singapore. doi: 10.1007/978-981-13-0080-6_12.
  • Luce, L. (2018). Artificial Intelligence For Fashion: How Ai is Revolutionizing the Fashion Industry. Apress, USA. Mueller, J. P. & Massaron, L. (2018). Artificial intelligence for dummies. John Wiley & Sons, Canada.
  • Özalp, E. (2020). Gençlerle Baş Başa Yapay Zekâ. İnkılap Kitapevi, İstanbul.
  • Özkan, İ. & Ülker, E. (2017). Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(3), 85-104.
  • Soomin, L. & Juhee, P. (2023). A Study on the Improvement of Fabric Property for Virtual Sample Using 3D Virtual Fashion CAD. Journal of the Korean Society of Costume, 73(1), 53-74.
  • Wang, Z., Tao, X., Zeng, X., Xing, Y., Xu, Z. & Bruniaux, P. (2023). Design of Customized Garments Towards Sustainable Fashion Using 3D Digital Simulation and Machine Learning-Supported Human–Product Interactions. International Journal of Computational Intelligence Systems, 16(16), 1-20. doi: 10.1007/s44196-023-00189-7.
  • Xinrong, H., Lei, C., Ruiqi, L., Junping, L., Jinxing, L., Tao, P, Li, L. & Hang, L. (2022). A Sketch-to-Clothing Image Generation Method Based on StyleGAN, Computer Science and Application, 12(10), 2405-2415. DOI: 10.12677/CSA.2022.1210246.
  • Yoshikawa, T., Endo, Y. & Kanamori, Y. (2023). StyleHumanCLIP: Text-guided Garment Manipulation for StyleGAN-Human. In International Conference on Computer Vision Theory and Applications, 1-12, Cornell University, New York.
  • İnternet Kaynakları Url-1. Bilim ve Teknik (2023). Yapay Zekâ. https://bilimteknik.tubitak.gov.tr/system/files/makale/yapayz.pdf, (Erişim tarihi: 25.10.2023).
  • Url 2. GenAi (2023). TRAI üretken yapay zekâ raporu https://turkiye.ai/wp content/uploads/2023/11/02 Gen AI Raporu_Ekim 2023.pdf, (Erişim tarihi: 10.12.2023).
  • Url 3. 9to5case (2023). https://www.9to5case.com/2022/07/fashioning future how materials science.html, (E-rişim tarihi: 10.12.2023).
  • Url-4. Textura (2024). https://textura.ai/product/,( Erişim tarihi: 05.03.2024).
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Textile and Fashion Design
Journal Section Reviews
Authors

Pınar Göklüberk Özlü 0000-0002-7050-3506

Nihal Ekici Demir 0000-0003-0633-4389

Publication Date June 28, 2024
Submission Date March 15, 2024
Acceptance Date April 30, 2024
Published in Issue Year 2024 Volume: 5 Issue: 8

Cite

APA Göklüberk Özlü, P., & Ekici Demir, N. (2024). Moda Tasarımı Sürecinde Üretken Yapay Zekâ Destekli Sistemler. STAR Sanat Ve Tasarım Araştırmaları Dergisi, 5(8), 110-122.