Innovative design of traditional calligraphy costume patterns based on deep learning. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Han, Chen; Lei, Shen; Mingming, Wang; Xiangfang, Ren; Xiying, Zhang
• Is Part Of: Journal of physics. Volume 1790:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1790/1/012029
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the global homogenization of today's costume design, a new design phenomenon is derived, that is, the globalization of regional ethnic traditional costume. Chinese traditional style has become one of the global fashion trends. In order to digitally inherit the traditional costume culture and innovate and upgrade the design process in the costume industry, this paper takes the traditional calligraphy costume patterns as the research object and proposes an innovative design method based on deep learning Generative Adversarial Network. This study analyzes the artistic characteristics and cultural genes of traditional calligraphic costume patterns, establishes a Generative Adversarial Network model containing discrimination and generation modules, and optimizes the design of the model for the characteristics of traditional costume patterns, such as small samples, multiple specifications, and emphasis on meaning rather than form. Through comparative experiment, subjective evaluation and design application, the advanced and practical value of the model are verified. This research aims to provide new ideas and methods for the inheritance and innovation of traditional culture in costume arts.