The color of composited pigments in digital painting is generally computed one of two ways: either alpha blending in RGB, or the Kubelka-Munk equation (KM). The former fails to reproduce paint like appearances, while the latter is difficult to use. We present a data-driven pigment model that reproduces arbitrary compositing behavior by interpolating sparse samples in a high dimensional space. The input is an of a color chart, which provides the composition samples. We propose two different prediction algorithms, one doing simple interpolation using radial basis functions (RBF), and another that trains a parametric model based on the KM equation to compute novel values. We show that RBF is able to reproduce arbitrary compositing behaviors, even non-paint-like such as additive blending, while KM compositing is more robust to acquisition noise and can generalize results over a broader range of values.

@article{Lu:2014:RPC,
   author = "Jingwan Lu and Stephen DiVerdi and Willa Chen and Connelly Barnes and
      Adam Finkelstein",
   title = "{RealPigment}: Paint Compositing by Example",
   journal = "NPAR 2014, Proceedings of the 12th International Symposium on
      Non-photorealistic Animation and Rendering",
   year = "2014",
   month = jun
}