a) color chart c John Perry (b) alpha blending (c) radial basis functions (d) Kubelka-Munk Figure 1: Color compositing: (a) a color chart crafted in marker in order to observe how different pigments appear when layered; (b) alpha blending, though standard in computer graphics, fails to capture known effects like yellow over blue gives green; (c) radial basis functions interpolate and extrapolate from the data in the chart to produce more realistic effects; (d) fitting parameters of a Kubelka-Munk model to the data in the chart improves on radial basis functions for paint-like behaviors. AbstractThe 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.