Optimizing the Digital Design Process for Lolita-Style Clothing Patterns Using the StyleGAN Image Generation Algorithm
Main Article Content
Abstract
With the increasing adoption of electromagnetic sensing, wireless imaging, and intelligent information acquisition technologies in digital fashion design and virtual manufacturing, high-quality pattern generation has become an important component of efficient visual information processing. To address the low efficiency of manual creativity and the difficulty of preserving cultural characteristics in the digital design of Lolita-style clothing patterns, this paper proposes an intelligent pattern generation framework based on an improved StyleGAN. A multi-scale style control architecture with a 12-layer mapping network and a hierarchical attention mechanism is developed to achieve fine-grained control of representative elements such as lace, ruffles, and decorative textures. Furthermore, a cultural feature constraint module combining VGG-19 feature alignment and Gram matrix style loss is introduced to preserve semantic consistency with Lolita aesthetic norms, while an improved adaptive instance normalization mechanism enhances local feature representation through grouped channel control and learnable normalization strength. A latent-space optimization strategy and seamless integration with CLO 3D further support efficient digital design and virtual garment development. Experimental results demonstrate that the proposed method achieves an average aesthetic score of 8.7, compresses the design cycle by 92.7%, and significantly reduces manual intervention while maintaining stable robustness under different noise conditions. The proposed framework provides an efficient and reliable solution for intelligent fashion pattern generation and offers methodological support for electromagnetic-enabled visual information acquisition, digital design systems, and multidisciplinary engineering applications involving advanced image processing and virtual modeling.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
Y. Zhang, “Rethinking Lolita Fashion: A Case Study in China,” Journal of Asia-Pacific Pop Culture. 2025; 10(1):55-70. doi: 10.5325/jasiapacipopcult.10.1.0055.
S. Gill, H. Al Houf, S. Hayes, and J. Conlon, “Evolving Pattern Practice, from Traditional Patterns to Bespoke Parametric Blocks,” International Journal of Fashion Design, Technology and Education. 2024; 17(2):144-161. doi: 10.1080/17543266.2023.2260829.
P. Bai, “A decolonial analysis of Lolita dressing practice and fashion in Mainland China,” Global Media and China. 2024; 9(1):52-68. doi: 10.1177/20594364231205081.
S. Y. Jang, C. Chung, and J. Ha, “A comparative study on the fashion design process utilizing shape memory textiles and conventional textiles: Implications for the industry and education,” Fashion and Textiles. 2025; 12(1):12. doi: 10.1186/s40691-025-00421-2.
H. E. Smith and K. P. Winterich, “Trendy or Classic? Increasing Sustainability by Increasing Preference for Style Durability,” Journal of Sustainable Marketing. 2024; 5(1):66-76. doi: 10.51300/JSM-2024-120.
J. Jung, H. Kim, and J. Park, “Deep Fashion Designer: Generative Adversarial Networks for Fashion Item Generation Based on Many-to-One Image Translation,” Electronics. 2025; 14(2):220. doi: 10.3390/electronics14020220.
D. Zhou, H. Zhang, J. Ma, and J. Shi, “BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing,” IEEE Transactions on Circuits and Systems for Video Technology. 2024; 34(5):3245-3259. doi: 10.1109/TCSVT.2023.3318216.
L. Fan, “Design and application of an intelligent generation model for fashion clothing images based on improved generative adversarial networks,” Service Oriented Computing and Applications. 2024; 1-14. doi: 10.1007/s11761-024-00429-4.
Y. Liu and M. Wu, “Intelligent Design of Ethnic Patterns in Clothing using Improved DCGAN for Real-Time Style Transfer,” International Journal of Advanced Computer Science & Applications. 2023; 14(11):1034-1044. doi: 10.14569/ijacsa.2023.01411105.
A. Imtiaz, N. Pathirana, S. Saheel, K. Karunanayaka, and C. Trenado, “A Review on the Influence of Deep Learning and Generative AI in the Fashion Industry,” Journal of Future Artificial Intelligence and Technologies. 2024; 1(3):201-216. doi: 10.62411/faith.3048-3719-29.
Z. Xiong, D. Kang, D. Jin, W. Chen, L. Bao, S. Cui, et al., “Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors,” Proceedings of 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 1-6 October 2023; Paris, France. New York, NY, USA: IEEE; 2023. p. 9253-9263. doi: 10.48550/arXiv.2302.01162.
R. Masukawa, S. Haji, T. Takagi, T. Matsui, K. Ishikawa, M. Fuchi, et al., “GAN-based Detailed Clothing Generation System,” IIAI Letters on Informatics and Interdisciplinary Research. 2023; 3. doi: 10.52731/liir.v003.067.
D. Zhou, H. Zhang, Q. Li, J. Ma, and X. Xu, “COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles,” IEEE Transactions on Multimedia. 2023; 25:4986-5001. doi: 10.1109/TMM.2022.3185894.
H. Yan, H. Zhang, J. Shi, J. Ma, and X. Xu, “Toward Intelligent Fashion Design: A Texture and Shape Disentangled Generative Adversarial Network,” ACM Transactions on Multimedia Computing, Communications and Applications. 2023; 19(3):107. doi: 10.1145/3567596.
W. Hu and Y. Zhang, “Research on Artistic Pattern Generation for Clothing Design Based on Style Transfer,” Journal of Combinatorial Mathematics and Combinatorial Computing. 2025; 127:4539-4550. doi: 10.61091/jcmcc127b-248.
J. Jiang, D. Wu, H. Deng, Y. Long, W. Tang, X. Li, et al., “HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2024; 8(3):107. doi: 10.1145/3678518.
L. Xiao and T. Yamasaki, “Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval,” IEEE Access. 2024; 12:48068-48080. doi: 10.1109/ACCESS.2024.3383785.
Y. Wan, G. Zou, C. Yan, and B. Zhang, “Dual attention composition network for fashion image retrieval with attribute manipulation,” Neural Computing and Applications. 2023; 35(8):5889-5902. doi: 10.1007/s00521-022-07994-9.
Y. Zheng, T. Zhang, J. Bao, D. Chen, and M. Zeng, “High-fidelity instructional fashion image editing,” Graphical Models. 2024; 135:101223. doi: 10.1016/j.gmod.2024.101223.
J. Sun, K. Cen, X. Ding, S. Haidar, and F. Zou, “A Study on Region-Aware Fashion Design Based on Probabilistic Diffusion Model,” IET Image Processing. 2025; 19(1):e70103. doi: 10.1049/ipr2.70103.
B. Suvarna and S. Balakrishna, “Enhanced content-based fashion recommendation system through deep ensemble classifier with transfer learning,” Fashion and Textiles. 2024; 11(1):24. doi: 10.1186/s40691-024-00382-y.
M. Adel, A. Elsayed, and A. Abdelatey, “A Fashion Recommendation System Based on Transfer Learning and Deep Ensemble Model: Enhancing Style Prediction,” IJCI. International Journal of Computers and Information. 2025; 12(2):30-46. doi: 10.21608/ijci.2024.331343.1181.
T. Fontanini and C. Ferrari, “Would Your Clothes Look Good on Me? Towards Transferring Clothing Styles with Adaptive Instance Normalization,” Sensors. 2022; 22(13):5002. doi: 10.3390/s22135002.
Q. Zhu, H. Bai, J. Sun, C. Cheng, and X. Li, “Lpadain: Light Progressive Attention Adaptive Instance Normalization Model for Style Transfer,” Electronics. 2022; 11(18):2929. doi: 10.3390/electronics11182929.
S. Huang, Q. Li, J. Liao, S. Wang, L. Liu, and L. Li, “Controllable image synthesis methods, applications and challenges: A comprehensive survey,” Artificial Intelligence Review. 2024; 57(12):336. doi: 10.1007/s10462-024-10987-w.