AI-Assisted Design: Intelligent Generation of Dong Paper-Cut Patterns

Abstract

Intelligent generation technology has been widely applied in the field of design, serving as an essential tool for many designers. This study focuses on the paper-cut patterns of Qin Naishiqing, an inheritor of Dong paper-cutting intangible cultural heritage, and explores the AI-assisted generation of Dong paper-cut patterns under designer–AI collaborative control. It proposes a new role for designers in human–AI collaborative design—the “designer-in-the-loop” model. From the perspective of dataset annotation, designers conduct visual feature analysis, Shape Factor Extraction, and Semantic Factor extraction of paper-cut patterns, actively participating in dataset construction, annotation, and collaborative control methods, including using localized LoRA for detail enhancement and creating controllable collaborative modes through contour lines and structural lines, evaluation of generated results, and iterative optimization. The experimental results demonstrate that the intelligent generation approach under the “designer-in-the-loop” model, combined with designer–AI controllable collaboration, effectively enhances the generation of specific-style Dong paper-cut patterns with limited sample data. This study provides new insights and practical methodologies for the intelligent generation of other stylistic patterns.