A Generic Pigment Model for Digital Painting
Identifieur interne : 004D99 ( Istex/Curation ); précédent : 004D98; suivant : 004E00A Generic Pigment Model for Digital Painting
Auteurs : Songhua Xu [République populaire de Chine] ; Haisheng Tan [République populaire de Chine] ; Xiantao Jiao [République populaire de Chine] ; Francis C. M. Lau [République populaire de Chine] ; Yunhe PanSource :
- Computer Graphics Forum [ 0167-7055 ] ; 2007-09.
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Abstract
We propose a generic pigment model suitable for digital painting in a wide range of genres including traditional Chinese painting and water‐based painting. The model embodies a simulation of the pigment‐water solution and its interaction with the brush and the paper at the level of pigment particles; such a level of detail is needed for achieving highly intricate effects by the artist. The simulation covers pigment diffusion and sorption processes at the paper surface, and aspects of pigment particle deposition on the paper. We follow rules and formulations from quantitative studies of adsorption and diffusion processes in surface chemistry and the textile industry. The result is a pigment model that spans a continuum from the very wet to the very dry brush stroke effects. We also propose a new pigment mixing method based on machine learning techniques to emulate pigment mixing in real life as well as to support the creation of new artificial pigments. To experiment with the proposed model, we embedded the model in a sophisticated digital brush system. The combined system exhibits interactive speed on a modest PC platform. http://www.cs.hku.hk/~songhua/pigment provides supplementary materials for this paper.
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DOI: 10.1111/j.1467-8659.2007.01084.x
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The model embodies a simulation of the pigment‐water solution and its interaction with the brush and the paper at the level of pigment particles; such a level of detail is needed for achieving highly intricate effects by the artist. The simulation covers pigment diffusion and sorption processes at the paper surface, and aspects of pigment particle deposition on the paper. We follow rules and formulations from quantitative studies of adsorption and diffusion processes in surface chemistry and the textile industry. The result is a pigment model that spans a continuum from the very wet to the very dry brush stroke effects. We also propose a new pigment mixing method based on machine learning techniques to emulate pigment mixing in real life as well as to support the creation of new artificial pigments. To experiment with the proposed model, we embedded the model in a sophisticated digital brush system. The combined system exhibits interactive speed on a modest PC platform. http://www.cs.hku.hk/~songhua/pigment provides supplementary materials for this paper.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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