Personalized Brand Visual Design Generation Combined with StyleGAN3

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.