  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.
