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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.
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.
We present results for multiple analytical BRDF models, rendered at
novelorientations and lighting conditions.
