Google Research

RealPigment: Paint Compositing by Example

  • Jingwan Lu
  • Stephen DiVerdi
  • Willa Chen
  • Connelly Barnes
  • Adam Finkelstein
Proceedings of the Workshop on Non-Photorealistic Animation and Rendering, NPAR, ACM, New York, NY, USA (2014), pp. 21-30


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.

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