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：为了给用户提供更好的虚拟现实体验，现有的重定向行走预测方法利用未来信息来减少重置次数。然而，此类方法在部署时通常需要施加前提条件（如虚拟环境的布局或用户的行走方向），这限制了其通用性。针对这一挑战，我们提出了一种新型机制F-RDW，它具有双重功能：（1）在无任何假设条件下预测用户在虚拟空间中的未来信息；（2）在操作现有重定向行走方法时融合这些信息。第一步的核心是一个基于LSTM的模型，该模型通过接收用户的空间数据与眼动跟踪数据，预测用户在虚拟空间中的未来位置；随后，第二步将预测值以兼容的方式输入现有重定向行走方法（如MPCRed、S2C、TAPF和ARC），同时尊重其内部机制。我们的仿真测试与用户研究结果表明，在狭小物理空间或复杂环境中使用重定向行走时，未来信息具有显著意义。我们证明，所提出的机制能够大幅减少重置次数并增加重置之间的行走距离，从而提升本研究中所有重定向行走方法的重定向性能。