AI-driven innovation in ethnic clothing design: an intersection of machine learning and cultural heritage

Abstract

This study delves into the innovative application of Artificial Intelligence (AI) and machine learning algorithms in the realm of ethnic fashion design, with a specific emphasis on the Miao women’s apparel. We introduce an AI-powered approach that strategically bridges modern technology with traditional elements, denoting a significant stride in the field of fashion design. Our research underscores three major aspects: customization of body shape, fabric selection, and innovative design. An AI-driven statistical methodology was utilized to accurately adapt to the unique body characteristics of Miao women, demonstrating an application of machine learning in pattern recognition. Furthermore, the AI’s capacity to analyze the fabric properties was harnessed to optimize material selection, creating a balance between aesthetics and comfort. The innovative use of the Multimodal Unsupervised Image-to-Image Translation (MUNIT) algorithm, an AI tool, generated diverse and trendy designs, thereby enriching the distinctiveness of ethnic apparel. Our study accentuates the synergistic blend of traditional crafting methods and modern technological applications, highlighting the role of AI in the sustainable development of ethnic fashion. Additionally, we also demonstrate the advantages of Made-to-Measure (MTM) approaches, emphasizing the importance of individual customization in contemporary fashion design. This research presents a pioneering exploration at the nexus of AI, pattern recognition, and ethnic fashion design, which has the potential to transform the future of the fashion industry.