Gloss perception in painterly and cartoon rendering
Name
Bousseau-GPI-2013-02.pdf
Description
Accepted version
Size
17.44 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Bousseau, Adrien
O'shea, James P.
Durand, Frederic
Ramamoorthi, Ravi
Agrawala, Maneesh
Date Issued
April 2013
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Bousseau, Adrien et al. "Gloss perception in painterly and cartoon rendering." ACM Transactions on Graphics 32, 2 (April 2013): 18. © 2013 ACM
Version
Author's final manuscript
Abstract
Depictions with traditional media such as painting and drawing represent scene content in a stylized manner. It is unclear, however, how well stylized images depict scene properties like shape, material, and lighting. In this article, we describe the first study of material perception in stylized images (specifically painting and cartoon) and use nonphotorealistic rendering algorithms to evaluate how such stylization alters the perception of gloss. Our study reveals a compression of the range of representable gloss in stylized images so that shiny materials appear more diffuse in painterly rendering, while diffuse materials appear shinier in cartoon images. From our measurements we estimate the function that maps realistic gloss parameters to their perception in a stylized rendering. This mapping allows users of NPR algorithms to predict the perception of gloss in their images. The inverse of this function exaggerates gloss properties to make the contrast between materials in a stylized image more faithful. We have conducted our experiment both in a lab and on a crowdsourcing Web site. While crowdsourcing allows us to quickly design our pilot study, a lab experiment provides more control on how subjects perform the task. We provide a detailed comparison of the results obtained with the two approaches and discuss their advantages and drawbacks for studies like ours.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2451236.2451244
