Exploring the Image Generation of Regional Public Brand IP Images from a Visuality Perspective
Abstract
This study investigates the design and generation of regional public brand IP images leveraging advanced Artificial Intelligence (AI) technologies. By integrating deep learning models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, we propose a novel computational framework for IP image creation that significantly enhances design efficiency and creative expression. The research highlights the transformative potential of AI in analyzing vast cultural datasets, recognizing intricate design patterns, and generating highly personalized and culturally resonant IP images. We detail the system architecture, which incorporates modules for cultural data ingestion, AI-powered design engine, and iterative user feedback. Experimental results demonstrate that AI-assisted design processes drastically reduce the time to completion compared to traditional methods, while also achieving higher scores in creativity and efficiency metrics. Furthermore, a comparative analysis of user feedback indicates superior performance of AI-generated designs in terms of design variety, cultural relevance, ease of use, and customization options. This paper provides a comprehensive technical exposition of the AI-driven methodology, offering theoretical guidance and practical support for the construction and promotion of local brands through computer-aided innovation. The findings underscore the critical role of computational approaches in modern design engineering, particularly for…
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