BatikGAN: A Generative Adversarial Network for Batik Creation
Wei-Ta Chu, Lin-Yu Ko · 2020
We propose a texture synthesis method based on generative adversarial networks, focusing on a cultural emblem, called Batik, of southeastern Asian countries. We propose a two-stage training approach to construct the network, first generating patches and then combining patches to generate the entire Batik image. Regular repetition and synthesis artifact removal are jointly considered to guide model training. In the evaluation, we show that the proposed generator fuses two Batik styles, removes blocking artifacts, and generates harmonious Batik images. Qualitative and quantitative evaluations are provided to show promising performance from several perspectives.