Journal of the Korean Society of Clothing and Textiles (한국의류학회지)

The Korean Society of Clothing and Textiles (한국의류학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Customized Textile Design using AI Technology -A Case of Korean Traditional Pattern Design-

  • Dawool Jung (Dept. of Fashion Design, College of Arts, Gachon University) ;
  • Sung-Eun Suh (Dept. of Fashion Design, College of Arts, Gachon University)
  • Received : 2023.08.30
  • Accepted : 2023.10.23
  • Published : 2023.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the advent of artificial intelligence (AI) during the Fourth Industrial Revolution, the fashion industry has simplified the production process and overcome the technical difficulties of design. This study anticipates likely changes in the digital age and develops a model that will allow consumers to design textile patterns using AI technology. Previous studies and industrial examples of AI technology's use in the textile design industry were investigated, and a textile pattern was developed using an AI algorithm. A new textile design model was then proposed based on its application to both virtual and physical clothing. Inspired by traditional Korean masks and props, AI technology was used to input color data from open application programming interface images. By inserting these into various repeating structures, a textile design was developed and simulated as garments for both virtual and real garments. We expect that this study will establish a new textile design development method for Generation Z, who favor customized designs. This study can inform the use of personalization in generative textile design as well as the systemization of technology-driven methods for customized and participatory textile design.

Keywords

References

  1. Abdi, A., & Safabakhsh, R. (2023). An automatic graphic pattern generation algorithm and its application to the multipurpose camouflage pattern design. IEEE Transactions on Cybernetics, 53(8), 4748-4762. https://doi.org/10.1109/TCYB.2022.3140394
  2. Acne studios X Robbie Barrat. (2020, January 22). Acne studios X Robbie barrat. Xrgoespop. https://xrgoespop.com/home/acne-studios-x-robbie-barrat
  3. Archibald, M. M., & Clark, A. M. (2023). ChatGTP: What is it and how can nursing and health science education use it? Journal of Advanced Nursing, 19(10), 3648-3651. https://doi.org/10.1111/jan.15643
  4. Arus, K., & Disaya, C. (2022). Developing design approaches for tile pattern designs inspired by traditional textile patterns. Processes, 10(23), 2744-2762. https://doi.org/10.3390/pr10122744
  5. Bain, M. (2023, June 20). The complete playbook for generative AI in fashion. Business of Fashion. https://www.businessoffashion.com/case-studies/technology/generative-ai-playbook-machine-learning-emerging-technology/
  6. Choufan, L. (2022). Fashion you do not own, fashion you cannot feel: Toward a new paradigm of sharing fashion in the digital age. Fashion Theory, 26(3), 307-328. https://doi.org/10.1080/1362704X.2021.1912954
  7. Duran, C., & Kilic, A. (2022). The role of the need for uniqueness in choosing between conventional/unconventional customization: The case of Gen Z. Journal of Management and Economics Research, 20(3), 325-350. https://doi.org/10.11611/yead.1146924
  8. Durand, G. (1976). L'imagination symbolique [Symbolic imagination]. Presses Universitaires de France.
  9. Francis, T., & Hoefel, F. (2018). True Gen: Generation Z and its implications for companies. McKinsey&Company. https://www.mckinsey.com/uk/~/media/McKinsey/Industries/Consumer%20Packaged%20Goods/Our%20Insights/True%20Gen%20Generation%20Z%20and%20its%20implications%20for%20companies/Generation-Z-and-its-implication-for-companies.pdf
  10. Fu, Y., & Liang, H. E. (2022). Sinicized exploration of sustainable digital fashion: Chinese game players' intention to purchase traditional costume skins. Sustainability, 14(13), 7877. https://doi.org/10.3390/su14137877
  11. Gooby, B. (2020). The development of methodologies for color printing in digital inkjet textile printing and the application of color knowledge in the ways of making project. Journal of Textile Design Research and Practice, 8(3), 358-383. https://doi.org/10.1080/20511787.2020.1827802
  12. Gong, W., & Khalid, L. (2021). Aesthetics, personalization and recommendation: A survey on deep learning in fashion. arXiv preprint arXiv:2101.08301. https://doi.org/10.48550/arXiv.2101.08301
  13. Hockly, N. (2011). The digital generation. ELT Journal, 65(3), 322-325. https://doi.org/10.1093/elt/ccr041
  14. Hong, C., Li, J., & Shin, K. (2020, July 2-4). Commercialization of clothing design technology using deep learning model: A case study of technology interface for user convenience in fashion industry [Paper presentation]. Korean Information Science Society Conference, Online. https://www.kiise.or.kr/academy/board/publishList2.fa?MENU_ID=060500
  15. Hunger, T. V. (2022, December 16). The internet is losing it over these Nike sneakers created by artificial intelligence. Hungertv. https://www.hungertv.com/editorial/the-internet-is-losing-it-over-these-nike-sneakers-created-by-artificial-intelligence
  16. Jeong, Y., Kim, K. C., & Sohn, C.-B. (2019). A Hanbok design using generative adversarial network: Focusing on DiscoGan, CycleGAN, and Munit. Korea Institute of Design Research Society, 4(3), 22-29.
  17. Jiang, S., Li, J., & Fu, Y. (2022). Deep learning for fashion style generation. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4538-4550. https://doi.org/10.1109/TNNLS.2021.3057892
  18. Jin, M. J. (2020, November 3). Lotte on offers computer-designed clothing. Korean JoongAng Daily. https://koreajoongangdaily.joins.com/2020/11/03/business/industry/lotte-lotte-shopping-lotte-on/20201103175006724.html
  19. Jose Maria, G., Valor, M., Albert, F., & Contero, M. (2003, July 2-4). Integrated system and methodology for supporting textile and tile pattern design [Paper presentation]. Smart Graphics 2003, Heidelberg, Germany.
  20. Kim, C. E., & Lee, J. H. (2018). Trends of big data and artificial intelligence in the fashion industry. Journal of the Korean Society of Clothing and Textiles, 42(1), 148-158. https://doi.org/10.5850/JKSCT.2018.42.1.148
  21. Kim, H. (2021). Classification and combination of fashion items using CNN-based deep learning. Journal of Digital Contents Society, 22(3), 475-482. https://doi.org/10.9728/dcs.2021.22.3.475
  22. Kim, J. (2015). A study on textile design process that applies algorithm [Unpublished doctoral dissertation]. Konkuk University.
  23. Oh, H. J., Kim, J., Chang, J. J. C., Park, N., & Lee, S. (2023). Social benefits of living in the metaverse: The relationships among social presence, supportive interaction, social selfefficacy, and feelings of loneliness. Computers in Human Behavior, 139, 107498. https://doi.org/10.1016/j.chb.2022.107498
  24. Lee, L. H., Braud, T., Hosio, S., & Hui, P. (2022). Towards augmented reality driven human-city interaction: Current research on mobile headsets and future challenges. ACM Computing Surveys, 54(8), 1-38. https://doi.org/10.1145/3467963
  25. Lee, W. Y. (2020). Fashion design education using Deep Dream Generator in intelligence information society. Journal of the Korean Society of Design Culture, 26(2), 429-446. https://doi.org/10.18208/ksdc.2020.26.2.429
  26. Liang, Y., Liu, C., & McRoberts, L. B. (2017). College students' acceptance of online mass-customized athletic shoes. Fashion and Textiles, 4, 15. https://doi.org/10.1186/s40691-017-0095-z
  27. Liu, L., Zhang, H., Ji, Y., & Wu, Q. J. (2019). Toward AI fashion design: An attribute-GAN model for clothing match. Neurocomputing, 341, 156-167. https://doi.org/10.1016/j.neucom.2019.03.011
  28. Lu, S., Mok, P. Y., & Jin, X. (2017). A new design concept: 3D to 2D textile pattern design for garments. Computer-Aided Design, 89, 35-49. https://doi.org/10.1016/j.cad.2017.03.002
  29. Makryniotis, T. (2018). Fashion and costume design in electronic entertainment: Bridging the gap between character and fashion design. Fashion Practice, 10(1), 99-118. https://doi.org/10.1080/17569370.2017.1412595
  30. Matthews, D., Rothenberg, L., & Gopalakrishnan, S. (2019). The impact of mass customization on fashion-innovative students: An assessment of need for uniqueness, self-identity, and perceived performance risk. International Journal of Fashion Design, Technology and Education, 12(3), 293-300. https://doi.org/10.1080/17543266.2019.1601263
  31. Melnyk, V. (2021, September 14-18). Punch card knitting pattern design in collaboration with GAN [Paper presentation]. The 12th International Conference on Computational Creativity, Mexico City, Mexico.
  32. Park, J., & Chun, J. (2023). Evolution of fashion as play in the digital space. Fashion Practice, 15(2), 1-23. https://doi.org/10.1080/17569370.2022.2149837
  33. Parsons, J. L. (2015). Historical patents as inspiration for digital textile and apparel design. Clothing and Textiles Research Journal, 33(4), 280-296. https://doi.org/10.1177/0887302X15600793
  34. Prensky, M. (2009). H. sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate: Journal of Online Education, 5(3). https://www.learntechlib.org/p/104264/ 104264/
  35. Ren, J., Chen, G., & Li, X. (2017). A fine grained digital textile printing system based on image registration. Computers in Industry, 92, 152-160. https://doi.org/10.1016/j.compind.2017.08.003
  36. Research and Market. (2022). Artificial intelligence (AI) in fashion: Global strategic business report. https://www.researchandmarkets.com/reports/5301848/artificial-intelligence-ai-in-fashion-global
  37. Sarmakari, N. (2023). Digital 3D fashion designers: Cases of Atacac and the Fabricant. Fashion Theory, 27(1), 85-114. https://doi.org/10.1080/1362704X.2021.1981657
  38. Seo, J. M., & Lee, W. J. (2022). A study on the improvement of complex characteristics of virtual try-on based on 2-d image by convergence of fashion and artificial intelligence. The Korean Society of Science & Art, 40(4), 193-203. http://doi.org/10.17548/ksaf.2022.09.30.193
  39. Snyder, C. R., & Fromkin, H. L. (1980). Uniqueness: The human pursuit of difference. Plenum Press.
  40. Son, J.-H. (2022, February 27). LG's AI fashion designer Tilda debuts in New York. The Korea Herald. https://www.koreaherald.com/view.php?ud=20220215000613
  41. Song, H. (2018). The making of microcelebrity: AfreecaTV and the younger generation in neoliberal South Korea. Social Media+Society,Media+Society, 4(4). https://doi.org/10.1177/2056305118814906
  42. Thomas, J. H., Roopashree, S., Joy, J. C., Kollarath, H. A., & Siby, A. (2022). Deep learning: A new way of looking into fashion. In V. Sharma, M. Singh, & J. Sinha (Eds.), Communication control and networking (pp. 1592-1595). Galgotias. https://doi.org/10.1109/ICAC3N56670.2022.10074341
  43. Tippett, B. G. (2002). The evolution and progression of digital textile printing. Digital Printing Systems Orangeburg. http://brookstippett.com/docs/Print2002-BGT.pdf
  44. Tyler, D. J. (2005). Textile digital printing technologies. Textile Progress, 37(4), 1-65. https://doi-org-ssl.glibproxy.gachon.ac.kr:8443/10.1533/tepr.2005.0004
  45. Turkut, u., Tuncer, A., Savran, H., & Yilmaz, S. (2020, June 26-27). An online recommendation system using deep learning for textile products [Paper presentation]. 2020 International Congress on Human-computer Interaction, Optimization and Robotic Applications, Turkey.
  46. Uh, K. J. (2020). Digital persona in E-girl and E-boy fashion images. Research Journal of the Costume Culture, 28(5), 692-704. https://doi.org/10.29049/rjcc.2020.28.5.692
  47. Valiente, J. M., Albert, F., Carretero, C., & Gomis, J. M. (2004, August 26-28). Structural description of textile and tile pattern designs using image processing [Paper presentation]. The 17th International Conference on Pattern Recognition, Cambridge, UK.
  48. Worbin, L. (2010). Designing dynamic textile patterns (Publication No. 2320/6283). [Doctoral dissertation, University of Boras]. ProQuest Dissertations and Theses Global.
  49. Wu, Q., Zhu, B., Yong, B., Wei, Y., Jiang, X., Zhou, R., & Zhou, Q. (2021). ClothGAN: Generation of fashionable Dunhuang clothes using generative adversarial networks. Connection Science, 33(2), 341-358. https://doi.org/10.1080/09540091.2020.1822780
  50. Yu, Z.-Y., & Luo, T.-J. (2021). Research on clothing patterns generation based on multi-scales self-attention improved generative adversarial network. International Journal of Intelligent Computing and Cybernetics, 14(4), 647-663. https://doi.org/10.1108/IJICC-04-2021-0065
  51. Zou, X., & Wong, W. (2021). Fashion after fashion: A report of AI in fashion. arXiv preprint arXiv:2105.03050. https://doi.org/10.48550/arXiv.2105.03050