SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis

01/09/2018
by   Wengling Chen, et al.
0

Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or require a database to retrieve images from. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes realistic looking 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 building block suit for both the generator and discriminator which improves the information flow and utilizes input images at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset