CoPaint: Guiding Sketch Painting with Consistent Color and Coherent Generative Adversarial Networks
Abstract: Art design plays an important role in attracting users. Thro- ugh art design, some sketches are more in line with aesthetics. Traditionally, we need to artificially color many series of black-and-white sketches using the same color, which is time-consuming and difficult for art designers. In addition, coherent sketch painting is challenging to automate. We propose a GAN-based approach CoPaint for sketch colorization. Our neural network takes as its input two black-and-white sketches with different rotation angles and produces a series of high-quality colored images of consistent color. We present an approach to generate a coherent sketch painting dataset. We also propose a paired generator network with shared weights that consists of convolutional layers and batch-normal layers. In addition, we propose a similarity loss that makes the images produced by the generator more similar. The provided experiments demonstrate the effectiveness of our approach.
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