Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2630397.2630401 | NPAR |
Keywords | Field | DocType |
paint,pigment,graphics utilities,compositing,color,kubelka munk | Alpha compositing,Radial basis function,Parametric model,Computer graphics (images),Computer science,Interpolation,RGB color model,Color chart,Digital painting,Compositing | Conference |
Citations | PageRank | References |
5 | 0.40 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingwan Lu | 1 | 218 | 17.00 |
| Stephen DiVerdi | 2 | 636 | 40.80 |
| Willa A. Chen | 3 | 5 | 0.40 |
| Connelly Barnes | 4 | 1729 | 59.07 |
| Adam Finkelstein | 5 | 4041 | 299.42 |