Structure–Color Coordinated Generation for Tang Dynasty Tuanke Patterns: Facilitating Living Conservation and Dissemination via Accessible Intelligent Design

Traditional Chinese patterns, as one of the core cultural heritage carriers, face inheritance challenges due to traditional design’s heavy reliance on professional expertise and lengthy cycles. The rise of Artificial Intelligence Generated Content (AIGC) has introduced a human-intelligence collaborative paradigm to address these issues—this article focuses on leveraging AIGC for intelligent design of traditional Chinese patterns. Specifically, a dataset of traditional Chinese patterns was first constructed, featuring 30,000 images represented by Tuanke patterns. Subsequently, ChiPa, an intelligent design method tailored to traditional Chinese patterns, was proposed. This method enables simultaneous control over both the content and color. ChiPa’s core is a novel Structure Projector (Feature Map Mixture + Latent Space Mapper with MCConv), which maps sketches to StyleGAN’s latent space to generate structurally accurate grayscale Tuanke patterns. For color customization, ChiPa employs an arbitrary style transfer module to render grayscale patterns via user-specified references. Extensive experiments validate ChiPa’s superiority: it outperforms SOTA methods in all metrics; 3 domain experts and 30 volunteers rated it highest in structural standardization, stylistic consistency, and symbolic accuracy. Case studies demonstrate applications in cultural (e.g., packaging) and commercial (e.g., sofa upholstery) products. ChiPa lowers public participation barriers, expands Tuanke patterns’ carriers, and promotes living conservation and dissemination of Chinese traditional cultural heritage.