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- research-articleDecember 2021
Practical pigment mixing for digital painting
ACM Transactions on Graphics (TOG), Volume 40, Issue 6Article No.: 234, Pages 1–11https://doi.org/10.1145/3478513.3480549There is a significant flaw in today's painting software: the colors do not mix like actual paints. E.g., blue and yellow make gray instead of green. This is because the software is built around the RGB representation, which models the mixing of colored ...
- posterAugust 2018
Perceptual-based CNN model for watercolor mixing prediction
SIGGRAPH '18: ACM SIGGRAPH 2018 PostersAugust 2018, Article No.: 14, Pages 1–2https://doi.org/10.1145/3230744.3230785In the poster, we propose a model to predict the mixture of water-color pigments using convolutional neural networks (CNN). With a watercolor dataset, we train our model to minimize the loss function of sRGB differences. In metric of color difference ΔE...
- articleOctober 1992
Modeling pigmented materials for realistic image synthesis
ACM Transactions on Graphics (TOG), Volume 11, Issue 4Pages 305–335https://doi.org/10.1145/146443.146452This article discusses and applies the Kubelka-Munk theory of pigment mixing to computer graphics in order to facilitate improved image synthesis. The theories of additive and subtractive color mixing are discussed and are shown to be insufficient for ...