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