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Data-Driven Iconification Yiming Liu, Aseem Agarwala, Jingwan Lu,
Given an input photo (top left) of a motorbike, the user sketches a polygon (top right) over the photo; this sketch, along with the keyword motorbike, are the only user inputs (the photo is not used). Our method remixes partially similar pictograms (bottom left) to create a pictogram (bottom right) that matches the user's sketch. Abstract Pictograms (icons) are ubiquitous in visual communication, but creating the best icon is not easy: users may wish to see a variety of possibilities before settling on a final form, and they might lack the ability to draw attractive and effective pictograms by themselves. We describe a system that synthesizes novel pictograms by remixing portions of icons retrieved from a large online repository. Depending on the user's needs, the synthesis can be controlled by a number of interfaces ranging from sketch-based modeling and editing to fully-automatic hybrid generation and scribble-guided montage. Our system combines icon-specific algorithms for salient-region detection, shape matching, and multi-label graph-cut stitching to produce results in styles ranging from line drawings to solid shapes with interior structure. Citation (BibTeX) Yiming Liu, Aseem Agarwala, Jingwan Lu, and Szymon Rusinkiewicz. Data-Driven Iconification. International Symposium on Non-Photorealistic Animation and Rendering (NPAR), May 2016. Paper Supplemental Material Awards |
