Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks

Abstract

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.

arXiv comment12 pages, 7 figures

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Generative Adversarial Text to Image Synthesis
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Unsupervised Image-to-Image Translation with Generative Adversarial Networks
• Scribbler: Controlling Deep Image Synthesis with Sketch and Color

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• Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
• Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
• Self-Supervised Sketch-to-Image Synthesis
• Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization
• Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks