EGWR: Eurographics Workshop on Rendering

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https://diglib.eg.org/handle/10.2312/378

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Browsing EGWR: Eurographics Workshop on Rendering by Subject "Arts and Humanities"

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    Artistic Illumination Transfer for Portraits
    (The Eurographics Association and Blackwell Publishing Ltd., 2012) Chen, Xiaowu; Jin, Xin; Zhao, Qinping; Wu, Hongyu; Fredo Durand and Diego Gutierrez
    Relighting a portrait in a single image is still a challenging problem, particularly when only a single artistic reference photograph or painting is provided. In this paper, we propose an artistic illumination transfer system for portraits based on a database of portrait images (photographs and paintings) associated with hand-drawn illumination templates (276) by artists. Users can select a reference portrait image in the database, and the corresponding illumination template is transferred to an input portrait using image warping. Users can also provide reference portrait images those are not in the database. Based on the Face Illumination Descriptor (FID), the system selects from the database the reference image with the closest illumination to that of the user-provided reference image and adjusts the corresponding illumination template to match the contrast of the user-provided reference image. Experiments on not only paintings but also photographs, paper-cuts and sketches demonstrate that convincing illumination transferred results can be rendered by our system.
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    Generating Pointillism Paintings Based on Seurat's Color Composition
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Wu, Yi-Chian; Tsai, Yu-Ting; Lin, Wen-Chieh; Li, Wen-Hsin; Nicolas Holzschuch and Szymon Rusinkiewicz
    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.