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首页> 外文会议>European Conference on Computer Vision(ECCV 2006) pt.1; 20060507-13; Graz(AT) >Real-Time Non-rigid Shape Recovery Via Active Appearance Models for Augmented Reality
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Real-Time Non-rigid Shape Recovery Via Active Appearance Models for Augmented Reality

机译：通过主动外观模型实现增强现实的实时非刚性形状恢复

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摘要

One main challenge in Augmented Reality (AR) applications is to keep track of video objects with their movement, orientation, size, and position accurately. This poses a challenging task to recover non-rigid shape and global pose in real-time AR applications. This paper proposes a novel two-stage scheme for online non-rigid shape recovery toward AR applications using Active Appearance Models (AAMs). First, we construct 3D shape models from AAMs offline, which do not involve processing of the 3D scan data. Based on the computed 3D shape models, we propose an efficient online algorithm to estimate both 3D pose and non-rigid shape parameters via local bundle adjustment for building up point correspondences. Our approach, without manual intervention, can recover the 3D non-rigid shape effectively from either real-time video sequences or single image. The recovered 3D pose parameters can be used for AR registrations. Furthermore, the facial feature can be tracked simultaneously, which is critical for many face related applications. We evaluate our algorithms on several video sequences. Promising experimental results demonstrate our proposed scheme is effective and significant for real-time AR applications.
机译：增强现实（AR）应用程序中的一项主要挑战是准确地跟踪视频对象的运动，方向，大小和位置。要在实时AR应用程序中恢复非刚性形状和全局姿势，这将是一项艰巨的任务。本文提出了一种采用主动外观模型（AAM）的面向AR应用的在线非刚性形状恢复的新的两阶段方案。首先，我们从AAM离线构建3D形状模型，该模型不涉及3D扫描数据的处理。基于计算出的3D形状模型，我们提出了一种有效的在线算法，可以通过局部束调整来估计3D姿势和非刚性形状参数，以建立点对应关系。我们的方法无需人工干预，就可以从实时视频序列或单个图像中有效地恢复3D非刚性形状。恢复的3D姿势参数可用于AR注册。此外，可以同时跟踪面部特征，这对于许多与面部相关的应用程序至关重要。我们在几个视频序列上评估我们的算法。有希望的实验结果表明，我们提出的方案对于实时AR应用是有效且有意义的。

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