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

September 24, 2019 · Entered Twilight · 🏛 IEEE International Conference on Computer Vision

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Repo contents: .gitignore, LICENSE, README.md, _config.yml, autocomplete, data, docs, imgs, main_gui_shadow_draw_color.py, main_gui_shadow_draw_pix2pix.py, main_gui_shadow_draw_sketch.py, main_gui_shadow_draw_sketchy.py, models, options, requirements.txt, scripts, test.py, train.py, ui_shadow_draw, util

Authors Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman arXiv ID 1909.11081 Category cs.CV: Computer Vision Cross-listed cs.LG, eess.IV Citations 144 Venue IEEE International Conference on Computer Vision Repository https://github.com/arnabgho/iSketchNFill ⭐ 194 Last Checked 7 days ago

Abstract

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/.