Color Hint-guided Ink Wash Painting Colorization with Ink Style Prediction Mechanism

Abstract

We propose an end-to-end generative adversarial network that allows for controllable ink wash painting generation from sketches by specifying the colors via color hints. To the best of our knowledge, this is the first study for interactive Chinese ink wash painting colorization from sketches. To help our network understand the ink style and artistic conception, we introduced an ink style prediction mechanism for our discriminator, which enables the discriminator to accurately predict the style with the help of a pre-trained style encoder. We also designed our generator to receive multi-scale feature information from the feature pyramid network for detail reconstruction of ink wash painting. Experimental results and user study show that ink wash paintings generated by our network have higher realism and richer artistic conception than existing image generation methods.