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 ap- pear 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.
Alex Limpaecher1, Nicolas Feltman1, Adrien Treuille1, and Michael Cohen2
1Carnegie Mellon 2Microsoft Research