A Robust Multilinear Model Learning Framework for 3D Faces

Timo Bolkart 1 Stefanie Wuhrer 2
2 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Multilinear models are widely used to represent the statistical variations of 3D human faces as they decouple shape changes due to identity and expression. Existing methods to learn a multilinear face model degrade if not every person is captured in every expression, if face scans are noisy or partially occluded, if expressions are erroneously labeled, or if the vertex correspondence is inaccurate. These limitations impose requirements on the training data that disqualify large amounts of available 3D face data from being usable to learn a multilinear model. To overcome this, we introduce the first framework to robustly learn a multilinear model from 3D face databases with missing data, corrupt data, wrong semantic correspondence , and inaccurate vertex correspondence. To achieve this robustness to erroneous training data, our framework jointly learns a multilinear model and fixes the data. We evaluate our framework on two publicly available 3D face databases, and show that our framework achieves a data completion accuracy that is comparable to state-of-the-art tensor completion methods. Our method reconstructs corrupt data more accurately than state-of-the-art methods, and improves the quality of the learned model significantly for erroneously labeled expressions.
Type de document :
Communication dans un congrès
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016, Las Vegas, United States. IEEE, pp.4911-4919, <10.1109/CVPR.2016.531>
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https://hal.inria.fr/hal-01290783
Contributeur : Timo Bolkart <>
Soumis le : vendredi 18 mars 2016 - 15:12:38
Dernière modification le : vendredi 3 février 2017 - 11:20:20
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Timo Bolkart, Stefanie Wuhrer. A Robust Multilinear Model Learning Framework for 3D Faces. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016, Las Vegas, United States. IEEE, pp.4911-4919, <10.1109/CVPR.2016.531>. <hal-01290783>

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