Scribbler: Controlling Deep Image Synthesis with Sketch and Color

TL;DR

This paper introduces Scribbler, a deep adversarial network that enables real-time, user-controlled image synthesis from sketches and color strokes, improving realism, diversity, and controllability over previous methods.

Contribution

It presents a novel, real-time, sketch- and color-guided deep image synthesis architecture that enhances user control and output quality compared to prior approaches.

Findings

Produces more realistic images than previous methods

Allows real-time editing and visualization

Effective at user-guided colorization of grayscale images

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.