F-RDW：基于未来位置预测的重定向行走
F-RDW: Redirected Walking with Forecasting Future Position
AI总结：
针对现有重定向行走方法受限于环境布局或行走方向的假设，提出F-RDW机制，利用基于LSTM的模型预测用户未来位置并无缝融合现有RDW方法，显著减少重置次数并提升重定向性能。
摘要：
为了向用户提供更好的VR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置发生的次数。然而，此类方法在部署时往往施加前提条件，要么在虚拟环境的布局上，要么在用户的行走方向上，这限制了其通用性。为了应对这一挑战，我们提出了一种新颖的双重机制F-RDW：（1）在没有任何假设的情况下预测用户在虚拟空间中的未来信息，以及（2）在操控现有RDW方法时融合这些信息。第一步的核心是一个基于LSTM的模型，它摄取用户的空间和眼动追踪数据来预测用户在虚拟空间中的未来位置，随后的步骤将这些预测值输入到现有的RDW方法（如MPCRed、S2C、TAPF和ARC）中，同时以适用的方式尊重它们的内部机制。我们的模拟测试和用户研究结果表明，在狭小物理空间或复杂环境中使用RDW时，未来信息具有重要意义。我们证明，所提出的机制显著减少了重置次数，并增加了两次重置之间的行走距离，从而增强了本研究中探讨的所有RDW方法的重定向性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
