Robust and Accurate 3D Head Pose Estimation through 3DMM and Online Head Model Reconstruction
| Type of publication: | Conference paper |
| Citation: | Yu_FG2017_2017 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) |
| Year: | 2017 |
| Crossref: | Yu_Idiap-RR-09-2017: |
| Abstract: | Accurate and robust 3D head pose estimation is important for face related analysis. Though high accuracy has been achieved by previous works based on 3D morphable model (3DMM), their performance drops with extreme head poses because such models usually only represent the frontal face region. In this paper, we present a robust head pose estimation framework by complementing a 3DMM model with an online 3D reconstruction of the full head providing more support when handling extreme head poses. The approach includes a robust on- line 3DMM fitting step based on multi-view observation samples as well as smooth and face-neutral synthetic samples generated from the reconstructed 3D head model. Experiments show that our framework achieves state-of-the-art pose estimation accuracy on the BIWI dataset, and has robust performance for extreme head poses when tested on natural interaction sequences. |
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| Projects |
UBIMPRESSED MUMMER |
| Authors | |
| Crossref by |
Yu_Idiap-RR-09-2017 |
| Added by: | [UNK] |
| Total mark: | 0 |
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