Big data and digital design models for fashion design

Abstract

The demand for fashion, and for virtual fitting and personalized fashion among customers, is changing the design and consumption of fashion. To meet such challenges, fashion design models are being developed based on big data and digitization, in which fashion is designed based on data, virtual fitting, design-support systems, and recommendation systems. This paper reviews the fashion design models proposed in recent years and considers future development directions for fashion design. Using big data and digital processing technologies, fashion designers identify the characteristics of popular fashions in the market, predict fashion trends, and create designs accordingly. The virtual fitting of scanatar, parametric mannequin, or even real human bodies, enables customers to quickly and easily find fashion that best meets their tastes and requirements. On consumer design-support platforms, consumers can freely select styles, colors, materials, and other fashion aspects and view the design output. Furthermore, fashion recommendation systems, guided by fashion design experts, have greatly improved consumer satisfaction with fashion design. Yet, current fashion design systems do not fully consider the performance of textile materials and do not involve functional fashion design, let alone comfort. Such limitations provide directions future research in fashion design.