Surface Motion Capture Transfer with Gaussian Process Regression - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Surface Motion Capture Transfer with Gaussian Process Regression

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.
Fichier principal
Vignette du fichier
SurfMoTransf.pdf (2.88 Mo) Télécharger le fichier
Vignette du fichier
teaser.jpg (525.19 Ko) Télécharger le fichier
68.mp4 (48.27 Mo) Télécharger le fichier
cvpr_poster.pdf (3.26 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Format Figure, Image
Origin Files produced by the author(s)
Format Video
Origin Files produced by the author(s)
Format Poster
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01491386 , version 1 (16-03-2017)
hal-01491386 , version 2 (02-08-2017)

Identifiers

Cite

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⟩
783 View
913 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More