Interactive sketch & fill: Multiclass sketch-to-image translation

Arnab Ghosh, Richard Zhang, Puneet Dokania, Oliver Wang, Alexei Efros, Philip Torr, Eli Shechtman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1171-1180
Number of pages10
ISBN (Electronic)9781728148038
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
Duration: Oct 27 2019Nov 2 2019

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2019-October
ISSN (Print)1550-5499

Conference

Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
CountryKorea, Republic of
CitySeoul
Period10/27/1911/2/19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Ghosh, A., Zhang, R., Dokania, P., Wang, O., Efros, A., Torr, P., & Shechtman, E. (2019). Interactive sketch & fill: Multiclass sketch-to-image translation. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 1171-1180). [9008845] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00126