移动AR应用的检测引导3D手部跟踪

Yunlong Che, Yue Qi
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引用次数: 2

摘要

人们对基于移动设备的增强现实(AR)越来越感兴趣。现有的基于rgb的作品不能提供一个实际的解决方案来识别手的丰富细节。在本文中,我们提出了一种检测引导的方法,能够用彩色相机恢复三维手部姿势。该方法由关键点检测器和三维姿态优化器组成。检测器首先定位2D手部边界框,然后在手部区域应用轻量级网络,以提供手部关节的像素似似度。优化器以模型拟合的方式从估计的2D关节中提升3D姿态。为了保证结果的可信性,我们将手形编码为目标函数。估计的3D姿态允许在AR应用程序中灵活的手对移动交互。我们在几个具有挑战性的公共数据集上广泛评估了所提出的方法。实验结果表明了该方法的有效性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection-Guided 3D Hand Tracking for Mobile AR Applications
Interaction using bare hands is experiencing a growing interest in mobile-based Augmented Reality (AR). Existing RGB-based works fail to provide a practical solution to identifying rich details of the hand. In this paper, we present a detection-guided method capable of recovery 3D hand posture with a color camera. The proposed method consists of key-point detectors and 3D pose optimizer. The detectors first locate the 2D hand bounding box and then apply a lightweight network on the hand region to provide a pixel-wise like-hood of hand joints. The optimizer lifts the 3D pose from the estimated 2D joints in a model-fitting manner. To ensure the result plausibly, we encode the hand shape into the objective function. The estimated 3D posture allows flexible hand-to-mobile interaction in AR applications. We extensively evaluate the proposed approach on several challenging public datasets. The experimental results indicate the efficiency and effectiveness of the proposed method.
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