In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applications. To tackle this challenge, we propose a novel mechanism F-RDW that is twofold: (1) forecasts the future information of a user in the virtual space without any assumptions, and (2) fuse this information while maneuvering existing RDW methods. The backbone of the first step is an LSTM-based model that ingests the user's spatial and eye-tracking data to predict the user's future position in the virtual space, and the following step feeds those predicted values into existing RDW methods (such as MPCRed, S2C, TAPF, and ARC) while respecting their internal mechanism in applicable ways.The results of our simulation test and user study demonstrate the significance of future information when using RDW in small physical spaces or complex environments. We prove that the proposed mechanism significantly reduces the number of resets and increases the traveled distance between resets, hence augmenting the redirection performance of all RDW methods explored in this work.
翻译:为了更好地为用户提供虚拟现实体验,现有的重定向行走(RDW)预测方法利用未来信息来减少重启次数。然而,这种方法通常在部署时会对虚拟环境的布局或用户的步行方向施加先决条件,这限制了它的通用应用。为了解决这个挑战,我们提出了一种新的机制 F-RDW,它具有双重功能:(1)不需要任何假设就可以预测用户在虚拟空间中的未来信息,(2)在操纵现有RDW方法时融合这些信息。第一步的支撑是一种基于LSTM模型的方法,它摄取用户的空间和眼动数据来预测用户在虚拟空间中的未来位置,随后将这些预测值输入到现有的RDW方法(例如MPCRed、S2C、TAPF和ARC)中,同时在适用的方式下尊重它们的内部机制。我们的模拟测试和用户研究结果证明,当在小的物理空间或复杂的环境中使用RDW时,未来信息的重要性。我们证明,所提出的机制显著减少了重启次数并增加了重启之间的行驶距离,从而增强了我们在本次研究中探索的所有RDW方法的重定向性能。