Cross-Platform Comparison of Generative Design Based on a Multi-Dimensional Cultural Gene Model of the Phoenix Pattern - Yali Wang; Xinxiong Liu; Yan Gan; Yixiao Gong; Yuchen Xi; Lin Li (Applied Sciences)
The rapid development of generative artificial intelligence has paved the way for a new approach to reproduce and intelligently generate traditional patterns digitally. This paper focuses on the traditional Chinese phoenix pattern and constructs a “Phoenix Pattern Multidimensional Cultural Gene Model” based on the grounded theory. It summarises seven semantic dimensions covering composition pattern, pixel configuration, colour system, media technology, semantic implication, theme context, and application scenario and divides them into explicit and implicit cultural genes. The study further proposes a control mechanism of “semantic label–prompt–image generation”, constructs a cross-platform prompt structure system suitable for Midjourney and Dreamina AI, and completes 28 groups of prompt combinations and six rounds of iterative experiments. The analysis of the results from 64 user questionnaires and 10 expert ratings reveals that Dreamina AI excels in cultural semantic restoration and context recognition. In contrast, Midjourney has an advantage in composition coordination and aesthetic consistency. Overall, the study verified the effectiveness of the cultural gene model in generating AIGC control. It proposed a framework for generating innovative traditional patterns, providing a theoretical basis and practical support for the intelligent expression of cultural heritage.