Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations
Using Factor Graphs

Stanford University
ACM Transactions on Graphics (Proc. SIGGRAPH 2013)


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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BibTeX citation:
author = {Sharon Lin and Daniel Ritchie and Matthew Fisher and Pat Hanrahan},
title = {Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs},
booktitle = {ACM SIGGRAPH 2013 papers},
series = {SIGGRAPH '13},
year = {2013}}