Abstract
We present a novel framework to inverse render faces in arbitrary complex illumination with a 3D morphable model. Compared to previously introduced methods, we specifically take self-occlusion into account and demonstrate that this improves the fitting accuracy by about 10%. Motivated by this observation, we design a generative statistical model of ambient occlusion. We examine generalisation error of the model and propose two ways how ambient occlusion can be inferred from shape. The proposed methods are incorporated into an existing framework to inverse render faces. We show qualitative and quantitative results for the proposed extensions and compare it with a reference method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, L., Samaras, D.: Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans. Pattern Anal. Mach. Intell. 28, 351–363 (2006)
Romdhani, S., Vetter, T.: Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In: Proc. CVPR, vol. 2, pp. 986–993 (2005)
Aldrian, O., Smith, W.A.P.: Inverse rendering in suv space with a linear texture model. In: ICCV Workshop, Color and Photometry in Computer Vision (2011)
Aldrian, O., Smith, W.A.P.: Inverse rendering with a morphable model: A multilinear approach. In: Proceedings of the British Machine Vision Conference, pp. 88.1–88.10. BMVA Press (2011)
Ramamoorthi, R., Hanrahan, P.: A signal-processing framework for inverse rendering. In: SIGGRAPH 2001: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 117–128. ACM, New York (2001)
Ramamoorthi, R.: Modeling illumination variation with spherical harmonics. In: Face Processing: Advanced Modeling and Methods. Academic Press (2005)
Visual Computing Laboratory, Institute of the National Research Council of Italy: Meshlab (2011), http://meshlab.sourceforge.net/
University of Southern California: High-resolution light probe image gallery (2011), http://gl.ict.usc.edu/Data/HighResProbes
Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann, Elsevier Science (2010)
Fletcher, P.T., Joshi, S., Lu, C., Pizer, S.M.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans. Med. Imaging 23, 995–1005 (2004)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61, 611–622 (1999)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. SIGGRAPH, pp. 187–194 (1999)
Smith, W.A.P., Hancock, E.R.: Recovering facial shape using a statistical model of surface normal direction. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1914–1930 (2006)
Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press (2006)
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: Proc. IEEE Intl. Conf. on Advanced Video and Signal based Surveillance (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aldrian, O., Smith, W.A.P. (2012). Model-Based Ambient Occlusion for Inverse Rendering. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31295-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-31295-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31294-6
Online ISBN: 978-3-642-31295-3
eBook Packages: Computer ScienceComputer Science (R0)