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Abstract:
An image-based acquisition pipeline for the reflective properties of a human
face is presented, proposing an approach which extracts geometry and a spatially
varying BRDF of a human face from few measurements in calibrated point light
conditions.
Applying the morphable face model introduced by Blanz-Vetter for the estimation
of geometry in each of the input images, we gain an image-based geometry
estimation which does not require range scans of the individual face, and obtain
a parameterization of the face surface, which is consistent for all human faces.
By means of non-linear optimization, we recover spatially varying parameters for
an analytic BRDF model; the resulting BRDF function, together with the estimated
geometry, creates a relightable representation of the face. The set of images
used for the reflectance measurement is identical to the set of images used for
the geometry estimation.
Several BRDF models (Cook-Torrance, Lafortune, Ward, and a custom
phe\-nom\-e\-no\-logical model) are compared in an experiment.
