Generating Pointillism Paintings Based on Seurat’s Color Composition


Our result (click to see the image in full size)

Input photo (click to see the original image)





This paper presents a novel example-based stippling technique that employs a simple and intuitive concept to convert a color image into a pointillism painting. Our method relies on analyzing and imitating the color distributions of Seurat’s paintings to obtain a statistical color model. Then, this model can be easily combined with the modified multi-class blue noise sampling to stylize an input image with characteristics of color composition in Seurat’s paintings. The blue noise property of the output image also ensures that the color points are randomly located but remain spatially uniform. In our experiments, the multivariate goodness-of-fit tests were adopted to quantitatively analyze the results of the proposed and previous methods, further confirming that the color composition of our results are more similar to Seurat’s painting style than that of previous approaches. Additionally, we also conducted a user study participated by artists to qualitatively evaluate the synthesized images of the proposed method.





·        Yi-Chian Wu, Yu-Ting Tsai, Wen-Chieh Lin, and Wen-Hsin Li, “Generating Pointillism Paintings Based on Seurat's Color Composition,” Eurographics Symposium on Rendering 2013.


Additional results


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