Kernel Projection of Latent Structures Regression for Facial Animation Retargeting
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Inspired by kernel methods that have been used extensively in achieving efficient facial animation retargeting, this paper presents a solution to retargeting facial animation in virtual character's face model based on the kernel projection of latent structure (KPLS) regression between semantically similar facial expressions. Specifically, a given number of corresponding semantically similar facial expressions are projected into the latent space. By using the Nonlinear Iterative Partial Least Square method, decomposition of the latent variables is achieved. Finally, the KPLS is achieved by solving a kernalized version of the eigenvalue problem. By evaluating our methodology with other kernel-based solutions, the efficiency of the presented methodology in transferring facial animation to face models with different morphological variations is demonstrated.
Description
@inproceedings{10.2312:vriphys.20171084,
booktitle = {Workshop on Virtual Reality Interaction and Physical Simulation},
editor = {Fabrice Jaillet and Florence Zara},
title = {{Kernel Projection of Latent Structures Regression for Facial Animation Retargeting}},
author = {Ouzounis, Christos and Kilias, Alex and Mousas, Christos},
year = {2017},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-032-1},
DOI = {10.2312/vriphys.20171084}
}