Experimental Study on Restoration and Color-Material-Finish Semantic Redesign of Ming-style Yazi Wooden Components Empowered by Generative AI

Abstract

This study focuses on the wooden spandrel components of Ming-style furniture to explore the application potential of generative artificial intelligence in the digital preservation and redesign of traditional woodworking cultural heritage. Based on the Dreamina AI platform, a multidimensional Prompt model integrating furniture category, form-feature, and CMF (Colour-Material-Finish) semantics was constructed. From the perspectives of material cognition and ecological reuse, a three-stage experimental path was designed: “Traditional wooden component restoration experiment—Trend CMF semantic experiment—Innovative CMF integrated redesign.” The CMF semantic experiment showed that different material and process semantic combinations had a significant impact on aesthetic and innovative perception (p<0.01), with the combination of “bamboo + green silk + phoenix embroidery” showing the best performance in terms of ecological aesthetics and cultural expression. The study concluded that generative AI under semantic control can achieve scientific and high-fidelity restoration of traditional components and extend innovative redesign through CMF semantic cultural extension. The openness and semantic construction capabilities of general generative artificial intelligence have introduced new digital expression methods to cultural heritage items made of natural materials, such as bamboo and wood. These methods are forming an interdisciplinary research paradigm that combines sustainable material restoration, cultural semantic control, and AI-driven design.