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.

客户对时装以及虚拟试穿和个性化时装的需求正在改变时装的设计和消费。为了应对这些挑战，正在基于大数据和数字化开发服装设计模型，其中基于数据，虚拟试衣，设计支持系统和推荐系统来设计服装。本文回顾了近年来提出的服装设计模型，并考虑了服装设计的未来发展方向。时装设计师使用大数据和数字处理技术，识别市场上流行的时装的特征，预测时装趋势，并据此进行设计。斯堪的纳维亚人，参数化人体模型甚至真实人体的虚拟拟合，使客户能够快速，轻松地找到最能满足其品味和要求的时尚。在消费者设计支持平台上，消费者可以自由选择样式，颜色，材料和其他时尚方面，并查看设计输出。此外，在服装设计专家的指导下，服装推荐系统极大地提高了消费者对服装设计的满意度。然而，当前的时装设计系统并未充分考虑纺织材料的性能，并且不涉及实用的时装设计，更不用说舒适性了。这些局限性为未来的时装设计研究提供了方向。大大提高了消费者对时装设计的满意度。然而，当前的时装设计系统并未充分考虑纺织材料的性能，并且不涉及实用的时装设计，更不用说舒适性了。这些局限性为未来的时装设计研究提供了方向。大大提高了消费者对时装设计的满意度。然而，当前的时装设计系统并未充分考虑纺织材料的性能，并且不涉及实用的时装设计，更不用说舒适性了。这些局限性为未来的时装设计研究提供了方向。