A Room to Roam: Reset Prediction Based on Physical Object Placement for Redirected Walking
Sulim Chun, Ho Jung Lee, In-Kwon Lee

TL;DR
This paper introduces a vision transformer-based model to predict reset frequency in redirected walking environments, enabling users to optimize object placement for fewer resets and enhanced VR experience.
Contribution
It presents a novel real-time reset prediction system using a vision transformer, facilitating rapid assessment of physical object placement to reduce resets in redirected walking.
Findings
Model achieved RMSE of 23.88 in reset prediction
Heatmaps reveal key regions influencing reset predictions
User interface allows instant visualization of reset changes
Abstract
In Redirected Walking (RDW), resets are an overt method that explicitly interrupts users, and they should be avoided to provide a quality user experience. The number of resets depends on the configuration of the physical environment; thus, inappropriate object placement can lead to frequent resets, causing motion sickness and degrading presence. However, estimating the number of resets based on the physical layout is challenging. It is difficult to measure reset frequency with real users repeatedly testing different layouts, and virtual simulations offer limited real-time verification. As a result, while rearranging objects can reduce resets, users have not been able to fully take advantage of this opportunity, highlighting the need for rapid assessment of object placement. To address this, in Study 1, we collected simulation data and analyzed the average number of resets for various…
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Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Prosthetics and Rehabilitation Robotics
