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Reflectance estimation of human face from a single shot image

Published: 21 July 2013 Publication History

Abstract

Simulation of the reflectance of translucent materials is one of the most important factors in the creation of realistic CG objects. Estimating the reflectance characteristics of translucent materials from a single image is a very efficient way of re-rendering objects that exist in real environments. However, this task is considerably challenging because this approach leads to problems such as the existence of many unknown parameters. Munoz et al. [2011] proposed a method for the estimation of the bidirectional surface scattering reflectance distribution function (BSSRDF) from a given single image. However, it is difficult or impossible to estimate the BSSRDF of materials with complex shapes because this method's target was the convexity of objects therefore, it used a rough depth recovery technique for global convex objects. In this paper, we propose a method for accurately estimating the BSSRDF of human faces, which have complex shapes. We use a 3D face reconstruction technique to satisfy the above assumption. We are able to acquire more accurate geometries of human faces, and it enables us to estimate the reflectance characteristics of faces.

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References

[1]
Jensen, H. W., Marschner, S. R., Levoy, M., and Hanrahan, P. 2001. A practical model for subsurface light transport. In Proc. SIGGRAPH 2001, 511--518.
[2]
Maejima, A., and Morishima, S. 2008. Fast Plausible 3D Face Generation from a Single Photograph. ACM SIGGRAPH ASIA 2008, Poster, maejima.pdf.
[3]
Munoz, A., Echevarria, J. I., Seron, F. J., Lopez-Moreno, J., Glencross, M., and Gutierrez, D. 2011. BSSRDF Estimation from Single Images. In Proc. Computer Graphics Forum 30, 2, 455--464.

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cover image ACM Conferences
SIGGRAPH '13: ACM SIGGRAPH 2013 Posters
July 2013
115 pages
ISBN:9781450323420
DOI:10.1145/2503385
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 21 July 2013

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