发表机构
University of Waterloo; Bishop’s University（滑铁卢大学; 毕晓普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用Transformer模型和OpenXR手部跟踪数据，实现实时手势识别，提升分类准确率，并展望手势转换检测。
AI 中文摘要
手势识别是人机交互（HCI）中的关键组成部分，可为游戏、虚拟现实（VR）、机器人等应用提供直观的界面。本研究将基于Transformer的机器学习模型集成到实时手势识别中，使用通过Unity中OpenXR标准捕获的手部跟踪数据。我们利用手部关节的位置数据和手腕旋转角度来训练自定义手势识别系统。通过利用Transformer的序列建模能力，系统能够捕获短手势窗口内的时序依赖关系，并在不同手部方向和尺寸下稳健地对手势进行分类。结果表明，手势分类准确率显著提升。在此基础上，我们概述了如何将该方法扩展至检测运动流，即手势之间的转换，作为未来工作。
英文摘要
Hand gesture recognition is a key component in human-computer interaction (HCI), enabling intuitive interfaces for applications in gaming, virtual reality (VR), robotics, and more. This study integrates transformer-based machine-learning models for real-time hand gesture recognition, using hand-tracking data captured through the OpenXR standard in Unity. We leverage positional data of hand joints and wrist rotation angles to train a custom gesture recognition system. By utilizing the sequential modeling capabilities of transformers, the system captures temporal dependencies within short gesture windows and classifies gestures robustly across hand orientations and sizes. The results show a significant improvement in gesture classification accuracy. Building on this, we outline how the approach can be extended toward detecting the flow of movement, i.e., the transitions between gestures, as future work.
