Auto-painter: Cartoon image generation from sketch by using conditional Wasserstein generative adversarial networks

作者：Liu, Yifan; Qin, Zengchang^*; Wan, Tao^*; Luo, Zhenbo

来源：Neurocomputing, 2018, 311: 78-87.

DOI：10.1016/j.neucom.2018.05.045

• Auto-painter
• GAN
• Wasserstein distance
• WGAN
• Deep learning
• Neural networks

Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Such an image can be generated at 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 useful application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose a model called auto-painter which can automatically generate compatible colors given a sketch. Wasserstein distance is used in training cGAN to overcome model collapse and enable the model converged much better. The new model is not only capable of painting hand-draw sketch with compatible colors, but also allowing users to indicate preferred colors. Experimental results on different sketch datasets show that the auto-painter performs better than other existing image-to-image methods.

• 出版日期2018-10-15
• 单位北京航空航天大学