Real-time drawing assistance through crowdsourcing

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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.
Funding Information
  • Division of Information and Intelligent Systems (IIS0953985)
  • Intel Corporation
  • Google
  • Adobe
  • Qualcomm
  • National Science Foundation
  • Okawa Foundation for Information and Telecommunications

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