Auto-painter: Cartoon image generation from sketch by using conditional Wasserstein generative adversarial 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 n...
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Published in | Neurocomputing (Amsterdam) Vol. 311; pp. 78 - 87 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
15.10.2018
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0925-2312 1872-8286 |
DOI: | 10.1016/j.neucom.2018.05.045 |