Gesture Knitter: A Hand Gesture Design Tool for Head-Mounted Mixed Reality Applications

George B. Mo, John J. Dudley, P. Kristensson
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引用次数: 17

Abstract

Hand gestures are a natural and expressive input method enabled by modern mixed reality headsets. However, it remains challenging for developers to create custom gestures for their applications. Conventional strategies to bespoke gesture recognition involve either hand-crafting or data-intensive deep-learning. Neither approach is well suited for rapid prototyping of new interactions. This paper introduces a flexible and efficient alternative approach for constructing hand gestures. We present Gesture Knitter: a design tool for creating custom gesture recognizers with minimal training data. Gesture Knitter allows the specification of gesture primitives that can then be combined to create more complex gestures using a visual declarative script. Designers can build custom recognizers by declaring them from scratch or by providing a demonstration that is automatically decoded into its primitive components. Our developer study shows that Gesture Knitter achieves high recognition accuracy despite minimal training data and delivers an expressive and creative design experience.
手势编织:一个用于头戴式混合现实应用的手势设计工具
手势是现代混合现实耳机支持的一种自然而富有表现力的输入法。然而,对于开发人员来说,为他们的应用程序创建自定义手势仍然是一个挑战。定制手势识别的传统策略包括手工制作或数据密集型深度学习。这两种方法都不适合新交互的快速原型。本文介绍了一种灵活有效的构建手势的替代方法。我们现在的手势编织:一个设计工具,用于创建自定义手势识别器与最小的训练数据。Gesture Knitter允许指定手势原语,然后可以使用可视化声明性脚本将这些原语组合起来创建更复杂的手势。设计人员可以通过从头开始声明或提供自动解码为其基本组件的演示来构建自定义识别器。我们的开发者研究表明,尽管训练数据很少,Gesture Knitter仍然实现了很高的识别精度,并提供了富有表现力和创造性的设计体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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