RealPigment: Paint Compositing by Example
Venue
Proceedings of the Workshop on Non-Photorealistic Animation and Rendering, NPAR, ACM, New York, NY, USA (2014), pp. 21-30
Publication Year
2014
Authors
Jingwan Lu, Stephen DiVerdi, Willa Chen, Connelly Barnes, Adam Finkelstein
BibTeX
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.
