Virtual reality (VR) interaction safety is a prerequisite for all user activities in the virtual environment. While seeking a deep sense of immersion with little concern about surrounding obstacles, users may have limited ability to perceive the real-world space, resulting in possible collisions with real-world objects. Nowadays, recent works and rendering techniques such as the Chaperone can provide safety boundaries to users but confines them in a small static space and lack of immediacy. To solve this problem, we propose a dynamic approach based on user motion prediction named SCARF, which uses Spearman's correlation analysis, rule learning, and few-shot learning to achieve prediction of user movements in specific VR tasks. Specifically, we study the relationship between user characteristics, human motion, and categories of VR tasks and provides an approach that uses biomechanical analysis to define the interaction space in VR dynamically.We report on a user study with 58 volunteers and establish a three dimensional kinematic dataset from a VR game. The experiments validate that our few-shot learning model is effective and can improve the performance of motion prediction. Finally, we implement SCARF in VR environment for dynamic safety boundary adjustment.

中文翻译：

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如何在虚拟现实中设置安全边界：基于用户运动预测的动态方法

虚拟现实（VR）交互安全是虚拟环境中所有用户活动的前提。在寻求深度沉浸感而不关心周围障碍物时，用户感知现实世界空间的能力可能有限，导致可能与现实世界物体发生碰撞。如今，最近的作品和渲染技术（例如Chaperone）可以为用户提供安全边界，但将他们限制在狭小的静态空间中，缺乏即时性。为了解决这个问题，我们提出了一种基于用户运动预测的动态方法SCARF，它利用Spearman的相关性分析、规则学习和少样本学习来实现特定VR任务中用户运动的预测。具体来说，我们研究了用户特征、人体运动和 VR 任务类别之间的关系，并提供了一种使用生物力学分析动态定义 VR 中交互空间的方法。我们报告了一项由 58 名志愿者参与的用户研究，并建立了三维运动学模型VR 游戏的数据集。实验验证了我们的少样本学习模型是有效的，并且可以提高运动预测的性能。最后，我们在VR环境中实现SCARF来进行动态安全边界调整。