J. Rafael Tena
Abstract
Linear models, particularly those based on principal component analysis (PCA), have been used successfully on a broad range of human face-related applications. Although PCA models achieve high compression, they have not been widely used for animation in a production environment because their bases lack a semantic interpretation. Their parameters are not an intuitive set for animators to work with. In this paper we present a linear face modelling approach that generalises to unseen data better than the traditional holistic approach while also allowing click-and-drag interaction for animation. Our model is composed of a collection of PCA sub-models that are independently trained but share boundaries. Boundary consistency and user-given constraints are enforced in a soft least mean squares sense to give flexibility to the model while maintaining coherence. Our results show that the region-based model generalises better than its holistic counterpart when describing previously unseen motion capture data from multiple subjects. The decomposition of the face into several regions, which we determine automatically from training data, gives the user localised manipulation control. This feature allows to use the model for face posing and animation in an intuitive style.
Supplemental Material
Available for Download
- Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: Reconstruction and parameterization from range scans. ACM Transactions on Graphics 22, 3 (July), 587--594. Google Scholar
Digital Library
- Bergeron, P., and Lachapelle, P. 1985. Controlling facial expressions and body movements in the computer-generated animated short "Tonly De Peltrie". In Computer Graphics, Advanced Computer Animation seminar notes.Google Scholar
- Black, M., and Yacoob, Y. 1995. Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. In Proceedings of the Fifth International Conference on Computer Vision, 374--381. Google ScholarDigital Library
- Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3D faces. In Proceedings of SIGGRAPH, 187--194. Google Scholar
- Buck, I., Finkelstein, A., Jacobs, C., Klein, A., Salesin, D. H., Seims, J., Szeliski, R., and Toyama, K. 2000. Performance-driven hand-drawn animation. In Proceedings of the 1st International Symposium on Non-photorealistic Animation and Rendering, 101--108. Google Scholar
- Cootes, T. F., Edwards, G. J., and Taylor, C. J. 1998. Active appearance models. In Proceedings of the 5th European Conference on Computer Vision, 484--498. Google ScholarDigital Library
- DeCarlo, D., and Metaxas, D. 2000. Optical flow constraints on deformable models with applications to face tracking. International Journal of Computer Vision 38, 2 (July), 99--127. Google ScholarDigital Library
- Dryden, I. L., and Mardia, K. V. 2002. Statistical Shape Analysis. John Wiley & Sons.Google Scholar
- Edwards, G. J., Cootes, T. F., and Taylor, C. J. 1998. Face recognition using active appearance models. In Proceedings of the 5th European Conference on Computer Vision, 581--595. Google ScholarDigital Library
- Ekman, P., and Friesen, W. V. 1978. Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto, CA.Google Scholar
- Feng, W.-W., Kim, B.-U., and Yu, Y. 2008. Real-time data driven deformation using kernel canonical correlation analysis. ACM Transactions on Graphics 27 (August), 91:1--91:9. Google ScholarDigital Library
- Joshi, P., Tien, W. C., Desbrun, M., and Pighin, F. 2003. Learning controls for blend shape based realistic facial animation. In Proceedings of ACM SIGGRAPH/Eurographics symposium on Computer Animation, 187--192. Google ScholarDigital Library
- Lau, M., Chai, J., Xu, Y.-Q., and Shum, H.-Y. 2009. Face poser: Interactive modeling of 3D facial expressions using facial priors. ACM Transactions on Graphics 29, 1 (Dec.), 3:1--3:17. Google ScholarDigital Library
- Lawrence, N. D. 2007. Learning for larger datasets with the gaussian process latent variable model. In International Workshop on Artificial Intelligence and Statistics. Google Scholar
- Lewis, J. P., and Anjyo, K. 2010. Direct manipulation blend-shapes. Computer Graphics and Applications, IEEE 30, 4 (July), 42--50. Google Scholar
- Matthews, I., and Baker, S. 2004. Active appearance models revisited. International Journal of Computer Vision 60 (November), 135--164. Google ScholarDigital Library
- Meyer, M., and Anderson, J. 2007. Key point subspace acceleration and soft caching. ACM Transactions on Graphics 26, 3 (July), 74:1--74:8. Google ScholarDigital Library
- Ng, A. Y., Jordan, M. I., and Weiss, Y. 2001. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, MIT Press, 849--856.Google Scholar
- Nishino, K., Nayar, S. K., and Jebara, T. 2005. Clustered blockwise PCA for representing visual data. IEEE Transactions on Pattern Analysis Machine Intelligence 27 (October), 1675--1679. Google ScholarDigital Library
- Noh, J.-Y., Fidaleo, D., and Neumann, U. 2000. Animated deformations with radial basis functions. In Proceedings of the ACM symposium on Virtual reality software and technology, 166--174. Google ScholarDigital Library
- Pentland, A., Moghaddam, B., and Starner, T. 1994. View-based and modular eigenspaces for face recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition, 84--91.Google Scholar
- Peyras, J., Bartoli, A., Mercier, H., and Dalle, P. 2007. Segmented AAMs improve person-independent face fitting. In British Machine Vision Conference.Google Scholar
- Pighin, F., Hecker, J., Lischinski, D., Szeliski, R., and Salesin, D. H. 1998. Synthesizing realistic facial expressions from photographs. In Proceedings of SIGGRAPH, 75--84. Google Scholar
- Sorkine, O., Cohen-Or, D., Lipman, Y., Alexa, M., Rössl, C., and Seidel, H.-P. 2004. Laplacian surface editing. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, 175--184. Google Scholar
- Tena, J. R., Hamouz, M., Hilton, A., and Illingworth, J. 2006. A validated method for dense non-rigid 3D face registration. In Proceedings of the IEEE International Conference on Video and Signal Based Surveillance. Google Scholar
- Tenenbaum, J. B., de Silva, V., and Langford, J. C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500, 2319--2323.Google Scholar
- Turk, M., and Pentland, A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 (January), 71--86. Google ScholarDigital Library
- Vlasic, D., Brand, M., Pfister, H., and Popović, J. 2005. Face transfer with multilinear models. ACM Transactions on Graphics 24, 3 (Aug.), 426--433. Google ScholarDigital Library
- Zhang, L., Snavely, N., Curless, B., and Seitz, S. M. 2004. Spacetime faces: High resolution capture for modeling and animation. ACM Transactions on Graphics 23, 3 (Aug.), 548--558. Google ScholarDigital Library
- Zhang, Q., Liu, Z., Guo, B., Terzopoulos, D., and Shum, H.-Y. 2006. Geometry-driven photorealistic facial expression synthesis. IEEE Transactions on Visualization and Computer Graphics 12 (January), 48--60. Google ScholarDigital Library
Index Terms
- Interactive region-based linear 3D face models
Recommendations
Interactive region-based linear 3D face models
SIGGRAPH '11: ACM SIGGRAPH 2011 papersLinear models, particularly those based on principal component analysis (PCA), have been used successfully on a broad range of human face-related applications. Although PCA models achieve high compression, they have not been widely used for animation in ...
3D-Aided Face Recognition Robust to Expression and Pose Variations
CVPR '14: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern RecognitionExpression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this paper, we aim to endow state of the art face recognition SDKs with robustness to facial expression variations and pose changes by using an extended 3D ...
Synthesis of a face image at a desired pose from a given pose
This paper proposes an efficient method to synthesize pose of an image. Pose synthesis is used to predict the face image with minimal error at a desired pose from a given pose. It is frequently required in many applications like production of animated ...
Comments