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Surface Motion Capture Transfer with Gaussian Process Regression

Adnane Boukhayma 1 Jean-Sébastien Franco 1 Edmond Boyer 1 
1 MORPHEO - Capture and Analysis of Shapes in Motion
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : We address the problem of transferring motion between captured 4D models. We particularly focus on human subjects for which the ability to automatically augment 4D datasets, by propagating movements between subjects, is of interest in a great deal of recent vision applications that builds on human visual corpus. Given 4D training sets for two subjects for which a sparse set of corresponding key-poses are known, our method is able to transfer a newly captured motion from one subject to the other. With the aim to generalize transfers to input motions possibly very diverse with respect to the training sets, the method contributes with a new transfer model based on non-linear pose interpolation. Building on Gaussian process regression, this model intends to capture and preserve individual motion properties, and thereby realism, by accounting for pose inter-dependencies during motion transfers. Our experiments show visually qualitative, and quantitative, improvements over existing pose-mapping methods and confirm the generalization capabilities of our method compared to state of the art.
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Adnane Boukhayma, Jean-Sébastien Franco, Edmond Boyer. Surface Motion Capture Transfer with Gaussian Process Regression. CVPR 2017 - IEEE Conference on Computer Vision and Pattern Recognition, Jul 2017, Honolulu, United States. pp.3558-3566, ⟨10.1109/CVPR.2017.379⟩. ⟨hal-01491386v2⟩



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