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Towards an articulated avatar in VR: Improving body and hand tracking using only depth cameras
Entertainment Computing ( IF 2.8 ) Pub Date : 2019-06-04 , DOI: 10.1016/j.entcom.2019.100303
Yuanjie Wu , Yu Wang , Sungchul Jung , Simon Hoermann , Robert W. Lindeman

An increasing number of virtual reality applications now use full-body avatars to represent the user in virtual environments. To fully control these virtual avatars, movement-tracking technology is required. However, most full-body tracking solutions are expensive and often cumbersome and time consuming to setup and use. Affordable depth cameras, on the other hand, are easy to set up, but most lack the ability to fully track a user’s body and fingers and have only limited accuracy. In this paper, we present a solution for combining multiple depth cameras to allow accurate full body movement tracking, including accurate hand and finger tracking. This provides users with the possibility of using natural gestures to interact in the virtual environment. In particular, we improve on previous work in the following five aspects. We have, (1) extended the calibration procedure to eliminate the tracking offsets between the RGB and depth cameras, (2) optimized facing-direction detection to improve the stability of data fusion, (3) implemented two new weighting methods for the depth data fusion of multiple cameras, (4) added the ability to also fuse joint-rotation data, and (5) integrated a short-range depth camera for finger tracking. We evaluated the system empirically and show that our new methods improved previous work in terms of tracking accuracy and particularly reduced the coupled hand-lifting phenomenon.



中文翻译:

迈向VR中的关节化身:仅使用深度摄像头改善身体和手部追踪

现在,越来越多的虚拟现实应用程序使用全身化身来代表虚拟环境中的用户。为了完全控制这些虚拟化身,需要运动跟踪技术。但是,大多数全身跟踪解决方案都很昂贵,并且设置和使用起来通常很麻烦且耗时。另一方面,价格适中的深度相机易于设置,但大多数相机都无法完全跟踪用户的身体和手指,并且准确性有限。在本文中,我们提出了一种结合多个深度相机的解决方案,以实现精确的全身运动跟踪,包括精确的手和手指跟踪。这为用户提供了使用自然手势在虚拟环境中进行交互的可能性。特别是,我们在以下五个方面改进了以前的工作。我们有,(1)扩展了校准程序,消除了RGB和深度相机之间的跟踪偏移;(2)优化了面向方向检测,以提高数据融合的稳定性;(3)为两种深度数据融合实施了两种新的加权方法摄像头,(4)增加了融合关节旋转数据的功能,(5)集成了用于手指跟踪的短程深度摄像头。我们根据经验对系统进行了评估,结果表明,我们的新方法在跟踪精度方面改善了以前的工作,特别是减少了手抬起耦合现象。(4)添加了还可以融合关节旋转数据的功能,并且(5)集成了用于手指跟踪的短程深度相机。我们根据经验对系统进行了评估,结果表明,我们的新方法在跟踪精度方面改善了以前的工作,特别是减少了手抬起耦合现象。(4)添加了还可以融合关节旋转数据的功能,并且(5)集成了用于手指跟踪的短程深度相机。我们根据经验对系统进行了评估,结果表明,我们的新方法在跟踪精度方面改善了以前的工作,特别是减少了手抬起耦合现象。

更新日期:2019-06-04
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