3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Arnab Ghosh 6:32 PM 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/.

          Related collections

          Most cited references8

          • Record: found
          • Abstract: not found
          • Conference Proceedings: not found

          Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

            Bookmark
            • Record: found
            • Abstract: not found
            • Book Chapter: not found

            Multimodal Unsupervised Image-to-Image Translation

              Bookmark
              • Record: found
              • Abstract: not found
              • Conference Proceedings: not found

              Fine-Grained Visual Comparisons with Local Learning

                Bookmark

                Author and article information

                Journal
                24 September 2019
                Article
                1909.11081
                e8228bf2-38ac-4912-81b0-97bb82d86331

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                ICCV 2019
                cs.CV cs.LG eess.IV

                Computer vision & Pattern recognition,Artificial intelligence,Electrical engineering

                Comments

                Comment on this article