Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138265
Title: Visibility constrained generative model for depth-based 3D facial pose tracking
Authors: Sheng, Lu
Cai, Jianfei
Cham, Tat-Jen
Pavlovic, Vladimir
Ngan, King Ngi
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2019
Source: Sheng, L., Cai, J., Cham, T.-J., Pavlovic, V., & Ngan, K. N. (2019). Visibility constrained generative model for depth-based 3D facial pose tracking. IEEE transactions on pattern analysis and machine intelligence, 41(8), 1994-2007. doi:10.1109/TPAMI.2018.2877675
Journal: IEEE transactions on pattern analysis and machine intelligence
Abstract: In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods.
URI: https://hdl.handle.net/10356/138265
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2018.2877675
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IMI Journal Articles

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