Data-Driven Iconification

International Symposium on Non-Photorealistic Animation and Rendering (NPAR)

Published May 1, 2016

Yiming Liu, Aseem Agarwala, Jingwan (Cynthia) Lu, Szymon Rusinkiewicz

Best paper award.

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.

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