Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation

International Conference on Computer Vision (ICCV'19)

Publication date: October 29, 2019

Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Alexei A. Efros, Philip H.S. Torr, Oliver Wang, Eli Shechtman

We propose an interactive GAN-based sketch-to-image translation method that helps novice users easily create images of simple objects. The user starts with a sparse sketch and a desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. This enables a feedback loop, where the user can edit the sketch based on the network's recommendations, while the network is able to better synthesize the image that the user might have in mind. In order to use a single model for 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.

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