Automatic Sewing Pattern Generation from Garment Images Using Segmentation and Conditional GANs

An automatic method for generating sewing patterns corresponding to dress images is proposed in this study. In garment production, the creation of sewing patterns, the blueprints for garment construction, from design sketches is a highly complex process that demands substantial expertise and experience. Most existing studies focus on learning from entire garments; however, they face the challenge of reduced shape reproduction accuracy for small parts with diverse shapes, such as collars and sleeves. The proposed method segments a garment image into three main parts—bodice, sleeve, and collar—and inputs each part into a specialized sewing pattern generation model, enabling faithful reproduction of even small and complex garment parts. A custom training dataset consisting of garment images and their corresponding sewing pattern images used in actual garment production is constructed. In addition, a part segmentation model and part-specific GAN-based sewing pattern generation models are developed. The proposed method is capable of adapting to diverse garment shapes and variations across parts, thereby enhancing both the accuracy and efficiency of sewing pattern creation in garment production workflows.