Fashion style transfer: enhancing efficiency and aesthetic with a novel two-stage neural network

Abstract

Neural style transfer is increasingly recognized as a crucial tool in fashion design. It enables the transfer of stylistic elements from an inspirational source image to a fashion product, thus streamlining the design process and enhancing efficiency. However, existing research primarily focuses on the transfer of painting styles, which only partially meets the specific requirements of fashion design. This study introduces a novel two-stage style transfer model for fashion design applications. This model can efficiently transfer fashion patterns that combine global and local features, preserving the original background while achieving symmetry and gradient effects in fashion patterns. Additionally, a five-factor machine scoring system, closely aligned with human preferences, is proposed to provide a more objective evaluation of the application value of style transfer models in fashion design. Our comparative experiments with various models demonstrate that our proposed model meets the specific requirements of fashion design, particularly regarding style transfer efficiency and aesthetic considerations.