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arXiv · 1801.02753

SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

Wengling Chen·James Hays
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Abstract

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or rely on retrieval of existing photographs. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes plausible images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new network building block suitable for both the generator and discriminator which improves the information flow by injecting the input image at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.

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Wengling Chen, James Hays. 2018-01-09. SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis. https://arxiv.org/abs/1801.02753

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