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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Robust and Accurate 3D Head Pose Estimation through 3DMM and Online Head Model Reconstruction
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Robust and Accurate 3D Head Pose Estimation through 3DMM and Online Head Model Reconstruction

机译：通过3DMM和在线头模型重建鲁棒精确的3D头姿态估计

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摘要

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 online 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.
机译：准确且鲁棒的3D头姿势估计对于面对相关分析至关重要。尽管基于3D可线模型（3DMM）的先前作品已经实现了高精度，但它们的性能下降，因为这种模型通常仅代表正面区域。在本文中，我们通过与在处理极端头部姿势时提供更多支撑的3DMM模型来介绍一种强大的头姿势估计框架。该方法包括基于多视图观察样本的稳健的在线3DMM拟合步骤，以及从重建的3D头模型产生的光滑和面部中性合成样本。实验表明，我们的框架在BIWI数据集上实现了最先进的姿态估计精度，并且在自然相互作用序列上测试时对极端头部姿势具有鲁棒性能。

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