Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138494
Title: A generative model for depth-based robust 3D facial pose tracking
Authors: Sheng, Lu
Cai, Jianfei
Cham, Tat-Jen
Pavlovic, Vladimir
Ngan, King Ngi
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Sheng, L., Cai, J., Cham, T.-J., Pavlovic, V., & Ngan, K. N. (2017). A generative model for depth-based robust 3D facial pose tracking. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4598-4607. doi:10.1109/CVPR.2017.489
Abstract: We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or data-driven methods that require sophisticated training or manual intervention, we propose a generative framework that unifies pose tracking and face model adaptation on-the-fly. Particularly, we propose a statistical 3D face model that owns the flexibility to generate and predict the distribution and uncertainty underlying the face model. Moreover, unlike prior arts employing the ICP-based facial pose estimation, we propose a ray visibility constraint that regularizes the pose based on the face models visibility against the input point cloud, which augments the robustness against the occlusions. The experimental results on Biwi and ICT-3DHP datasets reveal that the proposed framework is effective and outperforms the state-of-the-art depth-based methods.
URI: https://hdl.handle.net/10356/138494
ISBN: 978-1-5386-0458-8
DOI: 10.1109/CVPR.2017.489
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CVPR.2017.489.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IMI Conference Papers

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