A Room to Roam: Reset Prediction Based on Physical Object Placement for Redirected Walking

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 object placements. In study 2, we developed a system that allows users to evaluate reset frequency using a real-time placement interface powered by the first learning-based reset prediction model. Our model predicts resets from top-down views of the physical space, leveraging a Vision Transformer architecture. The model achieved a root mean square error (RMSE) of $23.88$. We visualized the model's attention scores using heatmaps to analyze the regions of focus during prediction. Through the interface, users can reorganize furniture while instantly observing the change in the predicted number of resets, thus improving their interior for a better RDW experience with fewer resets.