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Multilinear Wavelets: A Statistical Shape Space for Human Faces***
Alan Brunton1, Timo Bolkart2, 3, and Stefanie Wuhrer2
1Fraunhofer Institute for Computer Graphics Research IGD, Germany
2Cluster of Excellence MMCI, Saarland University, Germany
3Saarbrücken Graduate School of Computer Science, Germany
Abstract. We present a statistical model for 3D human faces in varying expression, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients. Using this model we are able to reconstruct faces from noisy and occluded 3D face scans, and facial motion sequences. Accurate reconstruction of face shape is important for applications such as tele-presence and gaming. The localized and multi-scale nature of our model allows for recovery of fine-scale detail while retaining robustness to severe noise and occlusion, and is computationally efficient and scalable. We validate these properties experimentally on challenging data in the form of static scans and motion sequences. We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.
Keywords: Statistical shape models, human faces, multilinear model, wavelets
Electronic Supplementary Material:
*This work has partially been funded by the German Research Foundation (DFG).
LNCS 8689, p. 297 ff.
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© Springer International Publishing Switzerland 2014