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  An Intuitive Control Space for Material Appearance

Serrano, A., Gutierrez, D., Myszkowski, K., Seidel, H.-P., & Masia, B. (2018). An Intuitive Control Space for Material Appearance. Retrieved from http://arxiv.org/abs/1806.04950.

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 Creators:
Serrano, Ana1, Author
Gutierrez, Diego1, Author
Myszkowski, Karol2, Author                 
Seidel, Hans-Peter2, Author                 
Masia, Belen1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Free keywords: Computer Science, Graphics, cs.GR
 Abstract: Many different techniques for measuring material appearance have been
proposed in the last few years. These have produced large public datasets,
which have been used for accurate, data-driven appearance modeling. However,
although these datasets have allowed us to reach an unprecedented level of
realism in visual appearance, editing the captured data remains a challenge. In
this paper, we present an intuitive control space for predictable editing of
captured BRDF data, which allows for artistic creation of plausible novel
material appearances, bypassing the difficulty of acquiring novel samples. We
first synthesize novel materials, extending the existing MERL dataset up to 400
mathematically valid BRDFs. We then design a large-scale experiment, gathering
56,000 subjective ratings on the high-level perceptual attributes that best
describe our extended dataset of materials. Using these ratings, we build and
train networks of radial basis functions to act as functionals mapping the
perceptual attributes to an underlying PCA-based representation of BRDFs. We
show that our functionals are excellent predictors of the perceived attributes
of appearance. Our control space enables many applications, including intuitive
material editing of a wide range of visual properties, guidance for gamut
mapping, analysis of the correlation between perceptual attributes, or novel
appearance similarity metrics. Moreover, our methodology can be used to derive
functionals applicable to classic analytic BRDF representations. We release our
code and dataset publicly, in order to support and encourage further research
in this direction.

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Language(s): eng - English
 Dates: 2018-06-132018
 Publication Status: Published online
 Pages: 12 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1806.04950
URI: http://arxiv.org/abs/1806.04950
BibTex Citekey: Serrano_arXiv1806.04950
 Degree: -

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Project name : CHAMELEON
Grant ID : 682080
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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