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Abstract:
In this paper, we present an image-based framework that acquires
the reflectance properties of a human face.
A range scan of the face is not required.
Based on a morphable face model, the system estimates the 3D
shape, and establishes point-to-point correspondence across images taken from
different viewpoints, and across different individuals' faces.
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This provides a common parameterization of all reconstructed surfaces that can
be used to compare and transfer BRDF data between different faces. Shape
estimation from images compensates deformations of the face during the
measurement process, such as facial expressions.
In the common parameterization, regions of homogeneous materials on the face
surface can be defined a-priori. We apply analytical BRDF models to express
the reflectance properties of each region, and we estimate their parameters in
a least-squares fit from the image data. For each of the surface points, the
diffuse component of the BRDF is locally refined, which provides high detail.
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We present results for multiple analytical BRDF models, rendered at novel
orientations and lighting conditions.
