2017/05/04 by Yifan Liu, Zengchang Qin, Liu, Yifan +5 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #I.3.3 #I.4.8 #I.4.9
paper · pdf · doi:10.48550/arxiv.1705.01908
openalex publication_date 2017/05/04 · openalex created_date 2017/05/12 · openalex updated_date 2026/07/28
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.