Optimizing Multi-Style Generation and Aesthetic Transfer Expression in Brand Visual Symbols Using StyleGAN
X. D. Zhang · Advanced Electromagnetics · 2026
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.