Probabilistic color-by-numbers
Probabilistic color-by-numbers
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DOI：
10.1145/2461912.2461988
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Sharon Lin;Daniel Ritchie;Matthew Fisher;P. Hanrahan
中科院分区：
文献类型：
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作者：
Sharon Lin;Daniel Ritchie;Matthew Fisher;P. Hanrahan
我们提出了一个概率因子图模型自动着色的二维模式。该模型在示例模式上进行训练，以统计地捕捉它们的风格属性。它包含了用于执行颜色兼容性和颜色空间排列的术语，这些术语与训练示例一致。使用马尔可夫链蒙特卡罗，可以对模型进行采样，为目标图案生成一组不同的新着色。这个通用的概率框架允许用户通过条件推理或附加的软约束来指导生成的建议。我们展示了各种着色任务的结果，我们通过感知研究评估模型，在该研究中，参与者判断采样着色明显优于其他自动基线。
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.