  We propose an interactive GAN-based sketch-to-image translation method that
helps novice users create images of simple objects. As the user starts to draw
a sketch of a desired object type, the network interactively recommends
plausible completions, and shows a corresponding synthesized image to the user.
This enables a feedback loop, where the user can edit their sketch based on the
network's recommendations, visualizing both the completed shape and final
rendered image while they draw. In order to use a single trained model across a
wide array of object classes, we introduce a gating-based approach for class
conditioning, which allows us to generate distinct classes without feature
mixing, from a single generator network. Video available at our website:
https://arnabgho.github.io/iSketchNFill/.
