Optimizing Multi-Style Generation and Aesthetic Transfer Expression in Brand Visual Symbols Using StyleGAN
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Abstract
To address the challenges of lengthy design cycles, limited stylistic diversity, and poor cross-scenario adaptability in traditional brand visual symbol design, this paper investigates the application of StyleGAN technology for optimizing multi-style generation and aesthetic transfer. As high-quality visual information generation and hierarchical feature representation become increasingly important in intelligent information processing and digital communication systems, the proposed framework provides a data-driven solution for controllable image synthesis and style manipulation. The study systematically analyzes the core advantages of StyleGAN in decoupling style and content control and hierarchical style injection, and establishes an application pipeline covering data preparation, model training, style regulation, and generation optimization to support multi-style generation and aesthetic transfer of brand visual symbols. Furthermore, the limitations of current approaches are analyzed, and future improvements in training strategies and quantitative aesthetic evaluation are discussed. Experimental analysis demonstrates that the StyleGAN-based framework effectively preserves brand identity while achieving flexible style transfer and high-quality image generation, providing theoretical and methodological support for the deep integration of artificial intelligence and visual design. The proposed approach also offers potential reference value for intelligent visual information processing and multi-scale feature representation in advanced engineering and electromagnetic information systems.
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References
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” in Sainath T, Murray I, editors. Proceedings of the 6th International Conference on Learning Representations, 30 April-3 May 2018, Vancouver, BC, Canada. Amherst, Massachusetts, USA: OpenReview.net, 2018, pp. 1-26, doi: 10.48550/arXiv.1710.10196.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 13-19 June 2020; Seattle, WA, USA; 2020. p. 8110-8119, doi: 10.1109/CVPR42600.2020.00813.
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, et al., “Alias-Free Generative Adversarial Networks,” in Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J, editors. NIPS ’21: Proceedings of the 35th International Conference on Neural Information Processing Systems, 6-14 December 2021, Red Hook, NY, USA, Curran Associates Inc., 2021, pp. 852-863, doi: 10.5555/3540261.3540327.
L. A. Gatys, A. S. Ecker, and M. Bethge, “Image Style Transfer Using Convolutional Neural Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 27-30 June 2016; Las Vegas, NV, USA. New York, NY, USA: IEEE; 2016. p. 2414-2423, doi: 10.1109/CVPR.2016.265.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution,” in Leibe B, Matas J, Sebe N, Welling M, editors. European Conference on Computer Vision, 11-14 October 2016, Amsterdam, The Netherlands. Cham, Switzerland: Springer, 2016, pp. 694-711, doi: 10.1007/978-3-319-46475-6_43.
W. J. Cao, “Research on Style Transfer and Automation in Visual Communication Design Based on Deep Learning,” Proceedings of the Second International Conference on Big Data, Computational Intelligence, and Applications; Bellingham, Wash, USA: Proceedings of the SPIE; 2025, 13550: 135503J, doi: 10.1117/12.3059636.
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” Proceedings of the IEEE International Conference on Computer Vision; 22-29 October 2017; Venice, Italy. New York, NY, USA: IEEE; 2017. p. 2223-2232, doi: 10.1109/ICCV.2017.244.
A. Radford, L. Metz, and S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” International Conference on Learning Representations; arXiv e-prints; 2016. arXiv:1511.06434, doi: 10.48550/arXiv.1511.06434.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 27-30 June 2016; Las Vegas, NV, USA. New York, NY, USA: IEEE; 2016. p. 770-778, doi: 10.1109/CVPR.2016.90.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., “Attention Is All You Need,” 31st Conference on Neural Information Processing Systems; 4-9 December 2017; Long Beach, CA, USA, pp. 5998–6008, 2017, doi: 10.48550/arXiv.1706.03762.
Y. Zhao, “Application of CycleGAN-based image style transfer algorithm in visual communication design[J],” Journal of Computational Methods in Sciences and Engineering, vol. 25, no. 4, pp. 3152-3164, 2025, doi: https://doi.org/10.1177/14727978251318804.
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 20-25 June 2009; Miami, FL, USA. New York, NY, USA: IEEE; 2009. p. 248-255, doi: 10.1109/CVPR.2009.5206848.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-Attention Generative Adversarial Networks,” in Chaudhuri K, Salakhutdinov R, editors. Proceedings of the 36th International Conference on Machine Learning. 9-15 June 2019, Long Beach, California, USA. mlr.press: PMLR, 2019, pp. 7354-7363, [Online]. Available: http://proceedings.mlr.press/v97/zhang19d.html.
Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang, “DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 27-30 June 2016; Las Vegas, NV, USA. New York, NY, USA: IEEE; 2016. p. 1096-1104, doi: 10.1109/CVPR.2016.124.
X. Zhang, “StyleGAN based path analysis from image to video generation in multimedia generation[J],” Discover Internet of Things, vol. 5, no. 1, pp. 1-21, 2025, doi: https://doi.org/10.1007/s43926-025-00233-2.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014, doi: 10.48550/arXiv.1409.1556.
A. J. Champandard, “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks,” arXiv preprint arXiv:1603.01768, 2016, doi: 10.48550/arXiv.1603.01768.
I. Kachbal and S. El Abdellaoui, “Computer Vision for Fashion: A Systematic Review of Design Generation, Simulation, and Personalized Recommendations[J],” Information, vol. 17, no. 1, pp. 11-60, 2025, doi: https://doi.org/10.3390/info17010011.
P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21-26 July 2017; Honolulu, HI, USA. New York, NY, USA: IEEE; 2017. p. 5967-5976, doi: 10.1109/CVPR.2017.632.
J. Liao, Y. Yao, L. Yuan, G. Hua, and S. B. Kang, “Visual Attribute Transfer through Deep Image Analogy,” ACM Transactions on Graphics, vol. 36, no. 4, pp. 120, 2017, doi: 10.1145/3072959.3073683.
Y. Choi, M. Choi, M. Kim, J. M. Ha, and J. Choo, “StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. New York, NY, USA: IEEE; 2018. p. 8789-8797, doi: 10.1109/CVPR.2018.00916.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-Local Neural Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 18-23 June 2018; Salt Lake City, UT, USA. New York, NY, USA: IEEE; 2018. p. 7794-7803, doi: 10.1109/cvpr.2018.00813.
X. Wu and L. Li, “Qualitative-empirical insights into generative AI for textile design in the fashion design process[J],” International Journal of Design Creativity and Innovation, vol. 13, no. 4, pp. 322-357, 2025, doi: https://doi.org/10.1080/21650349.2025.2529167.
A. Brock, J. Donahue, and K. Simonyan, “Large Scale GAN Training for High Fidelity Natural Image Synthesis,” in Brock A, Donahue J, Simonyan K, editors. International Conference on Learning Representations. 6-9 May 2019, New Orleans, LA, USA. OpenReview.net 2019, [Online]. Available: https://dblp.org/rec/conf/iclr/BrockDS19.html.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21-26 July 2017; Honolulu, HI, USA. New York, NY, USA: IEEE; 2017. P. 4700-4708, doi: 10.1109/CVPR.2017.243.
S. Xie and Z. Tu, “Holistically-Nested Edge Detection,” Proceedings of the IEEE International Conference on Computer Vision. 07-13 December 2015; Santiago, Chile. New York, NY, USA: IEEE; 2015. p. 1395-1403, doi: 10.1109/ICCV.2015.164.
X. Wang, Q. Zhang, Y. Nie, et al., “Towards photorealistic portrait style transfer in unconstrained conditions[J],” IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 10, pp. 6796-6809, 2025, doi: 10.1109/TVCG.2025.3529751.
W. Hu and Y. Zhang, “Research on artistic pattern generation for clothing design based on style transfer [J],” J. COMBIN MATH. COMBIN COMPUT, vol. 127, pp. 4539-4550, 2025, doi: 10.61091/jcmcc127b-248.