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Data-Driven Iconification
International Symposium on Non-Photorealistic Animation and Rendering (NPAR), May 2016

Yiming Liu, Aseem Agarwala, Jingwan Lu,
Szymon Rusinkiewicz


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
  PDF file

Supplemental Material
  Detailed Results of the Semi-Blind Test: PDF file
  More Results: PDF file

Awards
  Best Paper award at Expressive 2016