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 model's 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.
Original language | English |
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Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Editors | Yanxi Liu, James M. Rehg, Camillo J. Taylor, Ying Wu |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4598-4607 |
Number of pages | 10 |
ISBN (Electronic) | 9781538604571 |
ISBN (Print) | 9781538604588 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2017 - Honolulu, United States of America Duration: 21 Jul 2017 → 26 Jul 2017 http://cvpr2017.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/8097368/proceeding (Proceedings) |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2017 |
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Abbreviated title | CVPR 2017 |
Country/Territory | United States of America |
City | Honolulu |
Period | 21/07/17 → 26/07/17 |
Internet address |