Verse-in-Wine: A Generative AI Framework for Chinese Calligraphy Painting with Drinking Culture

This paper presents Verse-in-Wine, a generative framework that integrates Chinese classical poetry, traditional wine culture, and calligraphy painting through large language models (LLMs) and visual generation. Given user-selected intention keywords from culturally grounded categories, the system recommends poetic lines, maps them to symbolic wines and historical calligraphy styles, and synthesizes visually coherent outputs. A fully functional prototype was developed and evaluated through both automated and user studies. LLM-based evaluation across 300 samples achieved an overall score of 0.9165, while a user study with 100 samples yielded a comparable human rating of 0.8900, confirming both the system’s cultural fidelity and usability. The framework demonstrates how generative AI can meaningfully engage with heritage aesthetics, linking related cultures for artistic expression.