Design system of generative adversarial network (GAN) for bronze patterns based on transformer

Abstract

As an important part of Chinese traditional culture, the innovative design of bronze patterns has always been a research hotspot in the field of computer graphics. However, traditional design methods have been unable to meet contemporary needs, so new technologies are urgently needed to promote design innovation. This article aims to propose a Transformer based Generative Adversarial Network (GAN) for bronze pattern generation, and name it Trans GAN. This paper designs a joint network architecture that combines semantic segmentation and stereo matching, uses Swin-Transformer for feature extraction, and introduces multi-scale feature fusion and CGAN framework. Through end-to-end training, the adversarial process between the generator and the discriminator is optimized to improve the diversity and realism of pattern generation. Experiments have shown that Trans GAN reduces the Fr é chet Inception Distance (FID) score to 15.2 in bronze pattern generation tasks, generation accuracy reaches 92.3%, single image generation time is 0.12 s, and artificial style score is 8.7/10. Overall, Trans-GAN shows excellent performance in bronze pattern generation and provides a new solution for the digital protection and innovative design of cultural heritage.