Personalized Brand Visual Design Generation Combined with StyleGAN3
Main Article Content
Abstract
Current visual design for agricultural product brands often suffers from a lack of clear cultural semantic guidance, resulting in generic styles and blurred regional characteristics. Specifically, this manifests as a weak connection between design elements and the cultural connotations embodied by the brand, as well as insufficient semantic alignment with the regional cultural structure, making it difficult to accurately map regional cultural concepts into corresponding visual expressions. This article introduces a StyleGAN3 visual generation method guided by semantic embeddings to enhance the personalized expression of Hebei Province’s agricultural product brands (grain, fruits, vegetables, and livestock). It first employs TF-IDF and LDA to extract brand names, product categories, regional symbols, and value positioning, forming structured semantic vectors. These vectors are then hierarchically mapped and fused into the StyleGAN3 latent space via a semantic-driven intervention path to fine-tune visual elements like structure, texture, and color. Finally, a style transfer mechanism, using Hebei regional brand images as target samples, adjusts the StyleGAN3 output to ensure the stable expression of regional cultural visual characteristics. Experimental results confirm the method’s effectiveness, showing a maximum SSIM of 0.879, an average semantic consistency of 0.841, and an average FID score of 14.70. The conclusion demonstrates that the proposed method significantly improves the visual design effect, promoting the development of agricultural product brands in a more regional and personalized direction, a benefit that can also support surface-pattern and packaging-layout generation under clear semantic constraints.
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
A. R. Kwaku and Q. Fan, “Effect of good product design and packaging on market value and the performance of agricultural products in the Ghanaian market,” Open Access Library Journal, vol. 7, no. 9, pp. 1-14, 2020, doi: 10.4236/oalib.1106714.
D. Elwany, “Visual Treatments as Means of Visual Communication in Packaging Design to Enhance Brand Identity,” International Design Journal, vol. 15, no. 4, pp. 365-384, 2025, doi: 10.21608/idj.2025.379578.1320.
L. Yan and M. Na, “Role of Visual Communication in the Brand Marketing of Agricultural Products Qingzhou Dried Persimmon,” International Journal of Frontiers in Sociology, vol. 4, no. 3, pp. 26-35, 2022, doi: 10.25236/IJFS.2022.040305.
R. Farid, A. B. Pertiwi, Wahdiaman, and M. F. Benyamin, “Brand Design And Branding Strategy For Agro Nusantara “Kopi Warga”,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 11, pp. 1292-1301, 2021, doi: 10.17762/turcomat.v12i11.6035.
B. Andrade, R. Morais, and E. S. de Lima, “The personality of visual elements: A framework for the development of visual identity based on brand personality dimensions,” The International Journal of Visual Design, vol. 18, no. 1, pp. 67-98, 2024, doi: 10.18848/2325-1581/CGP/v18i01/67-98.
Y. Huang, “Research on the Brand Image Design of Agricultural Products under the Background of Rural Revitalization,” Design, vol. 9, no. 2, pp. 687-693, 2024, doi: 10.12677/design.2024.92261.
X. Xu, Z. Xu, L. Wu, K. Xu, and Y. Liu, “Research on the Packaging Design of Agricultural Products Based on the Regional Characteristics of Zhuji,” Interdisciplinary Science Letters, vol. 9, no. 1, pp. 40-45, 2025, doi: 10.12677/isl.2025.91006.
Z. Zhao, H. Zheng, and Y. Liu, “The appearance design of agricultural product packaging art style under the intelligent computer aid,” Computer-Aided Design and Applications, vol. 19, no. S3, pp. 164-173, 2022, doi: 10.14733/cadaps.2022.S3.164-173.
A. Manavis, A. Tzotzis, A. Tsagaris, and P. Kyratsis, “A novel computational-based visual brand identity (CbVBI) product design methodology,” Machines, vol. 10, no. 11, pp. 1065-1079, 2022, doi: 10.3390/machines10111065.
L. Lu, “Design of visual communication based on deep learning approaches: L,” Lu. Soft computing, vol. 24, no. 11, pp. 7861-7872, 2020, doi: 10.1007/s00500-019-03954-z.
S. Xiang and R. Gan, “A Machine Learning-Based Approach to Cross-Application of Computer Vision and Visual Communication Design for Automatic Labelling and Classification,” Informatica, vol. 49, no. 6, pp. 43-59, 2025, doi: 10.31449/inf.v49i6.6963.
M. Hou and Y. Tang, “The Influence of Visual Features in Product Images on Sales Volume: A Machine Learning Approach to Extract Color and Deep Learning Super Sampling Features,” Traitement du Signal, vol. 40, no. 4, pp. 1469-1477, 2023, doi: 10.18280/ts.400415.
Y. Cai, “Innovative design and development of brand visual system under the background of artificial intelligence-assisted visual communication design,” Discover Applied Sciences, vol. 7, no. 8, pp. 1-20, 2025, doi: 10.1007/s42452-025-07412-4.
X. Hou, Q. Liu, and X. Zhang, “Image processing algorithm of visual communication design based on deep learning in digital background,” Discover Artificial Intelligence, vol. 5, no. 1, pp. 1-17, 2025, doi: 10.1007/s44163-025-00430-6.
A. Sisodia, A. Burnap, and V. Kumar, “Generative interpretable visual design: Using disentanglement for visual conjoint analysis,” Journal of Marketing Research, vol. 62, no. 3, pp. 405-428, 2025, doi: 10.1177/00222437241276736.
Y. Tian, L. Shen, X. Tian, D. Tao, Z. Li, W. Liu, et al., “DGL-GAN: discriminator-guided GAN compression,” The Visual Computer, vol. 41, no. 7, pp. 1-22, 2024, doi: 10.1007/s00371-024-03682-y.
M. Kang, J. Shin, and J. Park, “StudioGAN: a taxonomy and benchmark of GANs for image synthesis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 15725-15742, 2023, doi: 10.1109/TPAMI.2023.3306436.
A. Burnap, J. R. Hauser, and A. Timoshenko, “Product aesthetic design: A machine learning augmentation,” Marketing Science, vol. 42, no. 6, pp. 1029-1056, 2023, doi: 10.1287/mksc.2022.1429.
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, vol. 14, no. 2, pp. 220-237, 2025, doi: 10.3390/electronics14020220.
J. Su, B. Yu, X. Li, and Z. Zhang, “A Style-Oriented Approach to Intelligent Generation of Product Creative Concepts,” Computer-Aided Design and Applications, vol. 21, no. 6, pp. 922-947, 2024, doi: 10.14733/cadaps.2024.922-947.
D. O. Esan and P. A. Owolawi, “Image Generation Using StyleVGG19-NST Generative Adversarial Networks,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 8, pp. 70-80, 2024, doi: 10.14569/IJACSA.2024.0150808.
J. Zuo, J. Hong, F. Zhang, C. Yu, H. Zhou, C. Gao, et al., “Plip: Language-image pre-training for person representation learning,” Advances in Neural Information Processing Systems, vol. 37, pp. 45666-45702, 2024, doi: 10.52202/079017-1452.
J. Lee, J. Kim, H. Shon, B. Kim, S. H. Kim, H. Lee, et al., “Uniclip: Unified framework for contrastive language-image pretraining,” Advances in Neural Information Processing Systems, vol. 35, pp. 1008-1019, 2022, doi: 10.52202/068431-0074.
R. Gal, O. Patashnik, H. Maron, A. H. Bermano, G. Chechik, and D. Cohen-Or, “Stylegan-nada: Clip-guided domain adaptation of image generators,” ACM Transactions on Graphics (TOG), vol. 41, no. 4, pp. 1-13, 2022, doi: 10.1145/3528223.3530164.
A. C. Baykal, A. B. Anees, D. Ceylan, E. Erdem, A. Erdem, and D. Yuret, “CLIP-guided StyleGAN inversion for text-driven real image editing,” ACM Transactions on Graphics, vol. 42, no. 5, pp. 1-18, 2023, doi: 10.1145/3610287.
B. Kang and Y. Jo, “StyleGAN-Based Advanced Semantic Segment Encoder for Generative AI,” IT Professional, vol. 26, no. 2, pp. 17-23, 2024, doi: 10.1109/MITP.2023.3338026.
H. Yan, H. Zhang, J. Shi, J. Ma, and X. Xu, “Inspiration transfer for intelligent design: A generative adversarial network with fashion attributes disentanglement,” IEEE Transactions on Consumer Electronics, vol. 69, no. 4, pp. 1152-1163, 2023, doi: 10.1109/TCE.2023.3255831.
M. Ma and W. Zhao, “Computer-Aided Brand Logo Design Based on Generative Adversarial Networks,” Computer-Aided Design and Applications, vol. 24, no. S25, pp. 60-75, 2024, doi: 10.14733/cadaps.2024.S25.60-75.
M. Wan and N. Jing, “Style recommendation and simulation for handmade artworks using generative adversarial networks,” Scientific Reports, vol. 14, no. 1, pp. 1-13, 2024, doi: 10.1038/s41598-024-79144-1.
W. Wu, K. Zheng, S. Ma, F. Lu, Y. Guo, Y. Zhang, et al., “Lotlip: Improving language-image pre-training for long text understanding,” Advances in Neural Information Processing Systems, vol. 37, pp. 64996-65019, 2024, doi: 10.52202/079017-2075.
H. Liu, M. Li, L. Li, D. Fu, and C. Xu, “Image style transfer based on generative adversarial networks,” Journal of Nanjing University of Information Science & Technology, vol. 15, no. 5, pp. 514-523, 2023.
W. Ding, J. Du, L. Hou, and J. Liu, “Fashion content and style transfer based on generative adversarial networks,” Journal of Computer Engineering & Applications, vol. 60, no. 9, pp. 261-271, 2024.
D. Fokina, E. Muravleva, G. Ovchinnikov, and I. Oseledets, “Microstructure synthesis using style-based generative adversarial networks,” Physical Review E, vol. 101, no. 4, pp. 1-16, 2020, doi: 10.1103/PhysRevE.101.043308.
X. Han, Y. Wu, and R. Wan, “A method for style transfer from artistic images based on depth extraction generative adversarial network,” Applied Sciences, vol. 13, no. 2, pp. 867-880, 2023, doi: 10.3390/app13020867.
W. Qian, H. Li, and H. Mu, “Circular LBP prior-based enhanced GAN for image style transfer,” International Journal on Semantic Web and Information Systems (IJSWIS), vol. 18, no. 2, pp. 1-15, 2022, doi: 10.4018/IJSWIS.315601.
Y. Zhu, H. Xu, A. Du, and B. Wang, “Image-Text Matching Model Based on CLIP Bimodal Encoding,” Applied Sciences, vol. 14, no. 22, pp. 10384-10398, 2024, doi: 10.3390/app142210384.
Y. Yu, W. Zhang, and Y. Deng, “Frechet inception distance (fid) for evaluating gans,” China University of Mining Technology Beijing Graduate School, vol. 3, no. 11, pp. 1-7, 2021.
C. I. Kim, M. Kim, S. Jung, and E. Hwang, “Simplified Fréchet distance for generative adversarial nets,” Sensors, vol. 20, no. 6, pp. 1548-1574, 2020, doi: 10.3390/s20061548.