Embroidery style generation with machine learning

Embroidery is an important intangible cultural heritage in China. The development of digital technology has changed the way of transmission and inheritance of traditional culture. At present, the research on digital simulation of embroidery is still relatively small, and there are some problems such as weak generalization ability and weak three-dimensional sense. According to the characteristics of embroidery art works, this paper proposes an embroidery style generation method combining attention mechanism and cycle-consistent adversarial networks. The attention mechanism module is used to guide the generator and discriminator to control the target area migration of embroidery style images, so as to digitally simulate the embroidery art style. The results show that the proposed method has stronger generalization ability than the traditional embroidery digital simulation method, and has greater optimization in embroidery reality compared with the existing deep learning model.