A Morphable Face Albedo Model
Name
2004.02711.pdf
Description
Accepted version
Size
9.76 MB
Format
Unknown
Checksum (MD5)
00c9fcf1e9bffcb8747d2b9c93724590
Author(s)
Smith, William A. P.
Seck, Alassne
Dee, Hannah
Tiddeman, Bernard
Tenenbaum, Joshua B
Egger, Bernhard
Date Issued
2020
Journal
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Smith, William AP, Seck, Alassane, Dee, Hannah, Tiddeman, Bernard, Tenenbaum, Joshua B et al. 2020. "A Morphable Face Albedo Model." Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Version
Author's final manuscript
Abstract
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 and we make our new albedo 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 Basel Face Model in albedo reconstruction.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Center for Brains, Minds, and Machines
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR42600.2020.00506
