Princeton > CS Dept > PIXL > Graphics > Publications Local Access 

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

Yiming Liu, Aseem Agarwala, Jingwan Lu,
Szymon Rusinkiewicz


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
  PDF file

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

Awards
  Best Paper award at Expressive 2016