In this paper, we bring together two divergent strands of research:
photometric face capture and statistical 3D face appearance modelling. We
propose a novel lightstage capture and processing pipeline for acquiring
ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor
out the effects of illumination, camera and geometry. Using this pipeline, we
capture a dataset of 50 scans and combine them with the only existing publicly
available albedo dataset (3DRFE) of 23 scans. This allows us to build the first
morphable face albedo model. We believe this is the first statistical analysis
of the variability of facial specular albedo maps. This model can be used as a
plug in replacement for the texture model of the Basel Face Model (BFM) or
FLAME and we make the model publicly available. We ensure careful spectral
calibration such that our model is built in a linear sRGB space, suitable for
inverse rendering of images taken by typical cameras. We demonstrate our model
in a state of the art analysis-by-synthesis 3DMM fitting pipeline, are the
first to integrate specular map estimation and outperform the BFM in albedo
reconstruction.Comment: CVPR 202