“Probabilistic color-by-numbers: suggesting pattern colorizations using factor graphs” by Lin, Ritchie, Fisher and Hanrahan

  • ©Sharon Lin, Daniel Ritchie, Matthew Fisher, and Patrick (Pat) Hanrahan




    Probabilistic color-by-numbers: suggesting pattern colorizations using factor graphs

Session/Category Title:   Color & Compositing




    We present a probabilistic factor graph model for automatically coloring 2D patterns. The model is trained on example patterns to statistically capture their stylistic properties. It incorporates terms for enforcing both color compatibility and spatial arrangements of colors that are consistent with the training examples. Using Markov Chain Monte Carlo, the model can be sampled to generate a diverse set of new colorings for a target pattern. This general probabilistic framework allows users to guide the generated suggestions via conditional inference or additional soft constraints. We demonstrate results on a variety of coloring tasks, and we evaluate the model through a perceptual study in which participants judged sampled colorings to be significantly preferable to other automatic baselines.


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