论文标题

Wristsketcher:用感应腕带在AR中创建动态素描

WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

论文作者

Ying, Enting, Xiong, Tianyang, Guo, Shihui, Qiu, Ming, Qin, Yipeng, Fu, Hongbo

论文摘要

受本地AR眼镜(例如触摸栏)的相互作用有限的相互作用区域的限制,在AR眼镜中创建草图是一项挑战。最近的作品试图使用移动设备(例如平板电脑)或中间裸手姿势来扩展交互式空间,并可以用作2D/3D素描AR眼镜的2D/3D素描输入接口。在它们之间,移动设备可以准确地绘制素描,但通常很重,而用裸手素描则是零负担,但由于手臂的不稳定性可能不准确。此外,空中徒手的素描很容易导致社会误解,并且长时间使用会导致手臂疲劳。作为一项新尝试,在这项工作中,我们提出了Warstketcher,这是一种基于灵活的感应腕带,用于创建2D动态草图,其中几乎为零荷兰式的创作模型,用于在现实世界中的精确和舒适的素描创建模型。具体而言,我们通过基于基于腕带上的传感压力点的手势识别方法来开发手势识别方法,简化了从空中到轻质感应腕带的表面的相互作用空间。我们的腕带使用的一组交互式手势是由对用户偏好的启发式研究确定的。此外,我们赋予Wristsketcher动画创建的能力,从而使其创建动态和表达的草图。实验结果表明,我们的手腕ketcher i)忠实地认识到用户的手势相互作用,高精度为96.0%; ii)比徒手素描获得更高的素描精度; iii)在易用性,可用性和功能方面达到了较高的用户满意度; iv)在艺术创作,记忆辅助和娱乐应用中展示了创新潜力。

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications.

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