Scribbler: Controlling Deep Image Synthesis
with Sketch and Color
 
Patsorn Sangkloy
 
Jingwan Lu
 
Chen Fang
 
Fisher Yu
 
James Hays
Abstract
Recently, there have been several promising methods to generate realistic imagery from deep convolutional networks. These methods sidestep the traditional computer graphics rendering pipeline and instead generate imagery at the pixel level by learning from large collections of photos (e.g. faces or bedrooms). However, these methods are of limited utility because it is difficult for a user to control what the network produces. In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces. We demonstrate a sketch based image synthesis system which allows users to ‘scribble’ over the sketch to indicate preferred color for objects. Our network can then generate convincing images that satisfy both the color and the sketch constraints of user. The network is feed-forward which allows users to see the effect of their edits in real time. We compare to recent work on sketch to image synthesis and show that our approach can generate more realistic, more diverse, and more controllable outputs. The architecture is also effective at user-guided colorization of grayscale images.
Citation
@article{
  sangkloy2016scribbler,
  title={Scribbler: Controlling Deep Image Synthesis with Sketch and Color},
  author={Sangkloy, Patsorn and Lu, Jingwan and Fang, Chen and Yu, FIsher and Hays, James},
  journal={Computer Vision and Pattern Recognition, CVPR},
  year={2017}
}
            
View Paper
Additional results
On our held out sketch:
On general sketch: : [ sketch ] [ sketch + color ]

More coming soon!
Code
github