Abstract
In this paper we consider the problem of inverse rendering faces under unknown environment illumination using a morphable model. In contrast to previous approaches, we account for global illumination effects by incorporating statistical models for ambient occlusion and bent normals into our image formation model. We show that solving for ambient occlusion and bent normal parameters as part of the fitting process improves the accuracy of the estimated texture map and illumination environment. We present results on challenging data, rendered under complex natural illumination with both specular reflectance and occlusion of the illumination environment.
Chapter PDF
Similar content being viewed by others
Keywords
- Principal Component Analysis Model
- Global Illumination
- Morphable Model
- Ambient Occlusion
- Illumination Environment
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25, 218–233 (2003)
Langer, M.S., Zucker, S.W.: Shape from shading on a cloudy day. JOSA-A 11, 467–478 (1994)
Langer, M.S., Büthoff, H.H.: Depth discrimination from shading under diffuse lighting (2000)
Prados, E., Jindal, N., Soatto, S.: A non-local approach to shape from ambient shading. In: Proc. IEEE Intl. Conf. on Scale Space and Variational Methods in Computer Vision, pp. 696–708. Springer (2009)
Zhukov, S., Iones, A., Kronin, F.: An ambient light illumination model. In: Rendering Techniques, Proceedings of the Eurographics Workshop. Springer (1998)
Landis, H.: Production-ready global illumination. Siggraph Course Notes 16 (2002)
Aldrian, O., Smith, W.A.P.: A linear approach of 3D face shape and texture recovery using a 3D morphable model. In: Proceedings of the British Machine Vision Conference, pp. 75.1–75.10. BMVA Press (2010)
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)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. SIGGRAPH, pp. 187–194 (1999)
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)
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)
Visual Computing Laboratory, Institute of the National Research Council of Italy (Meshlab), http://meshlab.sourceforge.net/
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61, 611–622 (1999)
Rasmussen, C.E., Williams, C.K.I.: Gaussian processes for machine learning. In: 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)
Pharr, M., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann. Elsevier Science (2010)
University of Southern California: High-resolution light probe image gallery (2011), http://gl.ict.usc.edu/Data/HighResProbes
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). Inverse Rendering of Faces on a Cloudy Day. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33712-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-33712-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33711-6
Online ISBN: 978-3-642-33712-3
eBook Packages: Computer ScienceComputer Science (R0)