  Recently, realistic image generation using deep neural networks has become a
hot topic in machine learning and computer vision. Images can be generated at
the pixel level by learning from a large collection of images. Learning to
generate colorful cartoon images from black-and-white sketches is not only an
interesting research problem, but also a potential application in digital
entertainment. In this paper, we investigate the sketch-to-image synthesis
problem by using conditional generative adversarial networks (cGAN). We propose
the auto-painter model which can automatically generate compatible colors for a
sketch. The new model is not only capable of painting hand-draw sketch with
proper colors, but also allowing users to indicate preferred colors.
Experimental results on two sketch datasets show that the auto-painter performs
better that existing image-to-image methods.
