A Morphable Face Albedo Model

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.

[1]  Shigeo Morishima,et al.  High-fidelity facial reflectance and geometry inference from an unconstrained image , 2018, ACM Trans. Graph..

[2]  Hans-Peter Seidel,et al.  FML: Face Model Learning From Videos , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Thabo Beeler,et al.  3D Morphable Face Models—Past, Present, and Future , 2020, ACM Trans. Graph..

[4]  Michael J. Black,et al.  Learning a model of facial shape and expression from 4D scans , 2017, ACM Trans. Graph..

[5]  Pieter Peers,et al.  Estimating Specular Roughness and Anisotropy from Second Order Spherical Gradient Illumination , 2009, Comput. Graph. Forum.

[6]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[7]  Paul E. Debevec,et al.  Multiview face capture using polarized spherical gradient illumination , 2011, ACM Trans. Graph..

[8]  Patrick Pérez,et al.  MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.

[10]  Stefanos Zafeiriou,et al.  GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[12]  William T. Freeman,et al.  Unsupervised Training for 3D Morphable Model Regression , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Pieter Peers,et al.  Rapid Acquisition of Specular and Diffuse Normal Maps from Polarized Spherical Gradient Illumination , 2007 .

[14]  William Smith,et al.  A 3D Morphable Model of Craniofacial Shape and Texture Variation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Pieter Peers,et al.  Circularly polarized spherical illumination reflectometry , 2010, ACM Trans. Graph..

[16]  Pieter Peers,et al.  Temporal upsampling of performance geometry using photometric alignment , 2010, TOGS.

[17]  Feng Liu,et al.  Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  William A. P. Smith,et al.  What Does 2D Geometric Information Really Tell Us About 3D Face Shape? , 2017, International Journal of Computer Vision.

[19]  T. Fitzpatrick The validity and practicality of sun-reactive skin types I through VI. , 1988, Archives of Dermatology.

[20]  Paul E. Debevec,et al.  Effect of illumination on automatic expression recognition: A novel 3D relightable facial database , 2011, Face and Gesture 2011.

[21]  Edwin R. Hancock,et al.  Seamless texture stitching on a 3D mesh by poisson blending in patches , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[22]  Stefanos Zafeiriou,et al.  Combining 3D Morphable Models: A Large Scale Face-And-Head Model , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Bernhard Egger,et al.  Markov Chain Monte Carlo for Automated Face Image Analysis , 2016, International Journal of Computer Vision.

[24]  Sabine Süsstrunk,et al.  What is the space of spectral sensitivity functions for digital color cameras? , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[25]  Bernhard Egger,et al.  Semantic Morphable Models , 2017 .

[26]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[27]  Sami Romdhani,et al.  A 3D Face Model for Pose and Illumination Invariant Face Recognition , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[28]  Xiaoming Liu,et al.  Nonlinear 3D Face Morphable Model , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Stefanos Zafeiriou,et al.  A 3D Morphable Model Learnt from 10,000 Faces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Thabo Beeler,et al.  High-quality single-shot capture of facial geometry , 2010, ACM Trans. Graph..

[31]  T. Fitzpatrick The validity and practicality of sun-reactive skin types I through VI. , 1988, Archives of dermatology.

[32]  Bernhard Egger,et al.  Morphable Face Models - An Open Framework , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[33]  Sami Romdhani,et al.  Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).