  Rubbing restorations are significant for preserving world cultural history.
In this paper, we propose the RubbingGAN model for restoring incomplete rubbing
characters. Specifically, we collect characters from the Zhang Menglong Bei and
build up the first rubbing restoration dataset. We design the first generative
adversarial network for rubbing restoration. Based on the dataset we collect,
we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore
the characters. The results of experiments show that RubbingGAN can repair both
slightly and severely incomplete rubbing characters fast and effectively.
