Real-time drawing assistance through crowdsourcing

  • Authors:
  • Alex Limpaecher;Nicolas Feltman;Adrien Treuille;Michael Cohen

  • Affiliations:
  • Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Microsoft Research

  • Venue:
  • ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
  • Year:
  • 2013

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Abstract

We propose a new method for the large-scale collection and analysis of drawings by using a mobile game specifically designed to collect such data. Analyzing this crowdsourced drawing database, we build a spatially varying model of artistic consensus at the stroke level. We then present a surprisingly simple stroke-correction method which uses our artistic consensus model to improve strokes in real-time. Importantly, our auto-corrections run interactively and appear nearly invisible to the user while seamlessly preserving artistic intent. Closing the loop, the game itself serves as a platform for large-scale evaluation of the effectiveness of our stroke correction algorithm.