Redirected Walking (RDW) is an important intermediary layer in virtual reality (VR) interaction systems. It addresses the issues of spatial restrictions in VR exploration by imperceptibly remapping the virtual environment’s movement to the physical environment, enhancing the user’s immersive experience. In VR, jumping is also a noteworthy motion besides walking. However, existing redirected walking (RDW) algorithms typically focus on reducing collisions between users and obstacles during walking but overlook the safety when users perform significant actions such as jumping. This oversight can pose serious risks to users during VR exploration, especially when there are physical obstacles or boundaries near the virtual locations that require user jumping. We propose SafeRDW, the first RDW algorithm that takes the user’s jumping safety into consideration. The proposed method considers both walking and jumping actions in the virtual environment, reducing physical resets and redirecting users to safer locations when a jump is required in the virtual space, ensuring user safety. Simulation experiments and user study results both show that our method not only reduces the number of resets, but also significantly ensures user safety when they reach the jumping points in the virtual scene.

Virtual Reality (VR) offers an immersive 3D digital environment, but enabling natural walking sensations without the constraints of physical space remains a technological challenge. Previous VR locomotion methods, including game controller, teleportation, treadmills, walking-in-place, and redirected walking (RDW), have made strides towards overcoming this challenge. However, these methods also face limitations such as possible unnaturalness, additional hardware requirements, or motion sickness risks. This paper introduces "Spatial Contraction (SC)", an innovative VR locomotion method inspired by the phenomenon of Lorentz contraction in Special Relativity. Similar to the Lorentz contraction, our SC contracts the virtual space along the user's velocity direction in response to velocity variation. The virtual space contracts more when the user's speed is high, whereas minimal or no contraction happens at low speeds. We provide a virtual space transformation method for spatial contraction and optimize the user experience in smoothness and stability. Through SC, VR users can effectively traverse a longer virtual distance with a shorter physical walking. Different from locomotion gains, the spatial contraction effect is observable by the user and aligns with their intentions, so there is no inconsistency between the user's proprioception and visual perception. SC is a general locomotion method that has no special requirements for VR scenes. The experimental results of our live user studies in various virtual scenarios demonstrate that SC has a significant effect in reducing both the number of resets and the physical walking distance users need to cover. Furthermore, experiments have also demonstrated that SC has the potential for integration with existing locomotion techniques such as RDW.

With redirected walking (RDW) technology, people can explore large virtual worlds in smaller physical spaces. RDW controls the trajectory of the user's walking in the physical space through subtle adjustments, so as to minimize the collision between the user and the physical space. Previous predictive algorithms place constraints on the user's path according to the spatial layouts of the virtual environment and work well when applicable, while reactive algorithms are more general for scenarios involving free exploration or uncon-strained movements. However, even in relatively free environments, we can predict the user's walking to a certain extent by analyzing the user's historical walking data, which can help the decision-making of reactive algorithms. This paper proposes a novel RDW method that improves the effect of real-time unrestricted RDW by analyzing and utilizing the user's historical walking data. In this method, the physical space is discretized by considering the user's location and orientation in the physical space. Using the weighted directed graph obtained from the user's historical walking data, we dynamically update the scores of different reachable poses in the physical space during the user's walking. We rank the scores and choose the optimal target position and orientation to guide the user to the best pose. Since simulation experiments have been shown to be effective in many previous RDW studies, we also provide a method to simulate user walking trajectories and generate a dataset. Experiments show that our method outperforms multiple state-of-the-art methods in various environments of different sizes and spatial layouts.