Research on simulation and optimization algorithm of watercolor paint mixing effect based on graph neural network

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Proposed algorithm improves watercolor pigment simulation, reducing color difference by 15.3% and computation time by 40%.

The proposed algorithm achieves a reduction in color difference ΔE value by 15.3%, improving color accuracy in watercolor mixing.
Significantly decreases calculation time by around 40%, enhancing efficiency for digital art applications.
Graph neural network captures both local and global information during pigment mixing, providing superior simulation performance.
Results indicate clear advantages over traditional physical simulation methods, specifically in visual effect and computational speed.

Weijian Zhang (2025) studied this question.

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