Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
This addresses a specific problem in digital entertainment for generating cartoon images from sketches, with incremental improvements over prior methods.
The paper tackles the problem of generating colorful cartoon images from black-and-white sketches using conditional generative adversarial networks (cGAN), resulting in an auto-painter model that outperforms existing image-to-image methods on two sketch datasets.
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.