RealPigment: Paint Compositing by Example

Non-Photorealistic Animation and Rendering (NPAR)

Publication date: June 1, 2014

Jingwan (Cynthia) Lu, Stephen DiVerdi, W. Chen, C. Barnes, A. Finkelstein

Best paper award

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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Research Area:  Adobe Research iconGraphics (2D & 3D)