Modeling the Impact of Visual Stimuli on Redirection Noticeability with Gaze Behavior in Virtual Reality

TL;DR

The paper tackles how environmental visual stimuli in VR influence the noticeability of redirected avatar motion by leveraging gaze behavior as a predictive signal. It collects dual-task data to link gaze features (IPA, saccades, fixations, gaze distance) with noticeability, builds a regression model achieving an MSE around $0.011$ to $0.014$ on unseen stimuli, and demonstrates an adaptive redirection technique that reduces physical demand while enhancing embodiment and agency. The work introduces contextualized, gaze-driven control for redirection that generalizes across new stimuli and supports real-world applications like adaptive VR games and opportunistic rendering. Overall, it advances practical, user-aware redirection in VR by coupling perception, attention, and motion manipulation in real-time.

Abstract

While users could embody virtual avatars that mirror their physical movements in Virtual Reality, these avatars' motions can be redirected to enable novel interactions. Excessive redirection, however, could break the user's sense of embodiment due to perceptual conflicts between vision and proprioception. While prior work focused on avatar-related factors influencing the noticeability of redirection, we investigate how the visual stimuli in the surrounding virtual environment affect user behavior and, in turn, the noticeability of redirection. Given the wide variety of different types of visual stimuli and their tendency to elicit varying individual reactions, we propose to use users' gaze behavior as an indicator of their response to the stimuli and model the noticeability of redirection. We conducted two user studies to collect users' gaze behavior and noticeability, investigating the relationship between them and identifying the most effective gaze behavior features for predicting noticeability. Based on the data, we developed a regression model that takes users' gaze behavior as input and outputs the noticeability of redirection. We then conducted an evaluation study to test our model on unseen visual stimuli, achieving an accuracy of 0.012 MSE. We further implemented an adaptive redirection technique and conducted a proof-of-concept study to evaluate its effectiveness with complex visual stimuli in two applications. The results indicated that participants experienced less physical demanding and a stronger sense of body ownership when using our adaptive technique, demonstrating the potential of our model to support real-world use cases.

Figures (13)

• Figure 1: The apparatus of the formative study. Wearing a headset, the participant wears three motion trackers to track their arm pose and sit on a comfortable chair. While the virtual avatar mirrors the arm movement of the participant, the participant observes the virtual avatar's movement from a first-person point of view and follows the semi-transparent checkpoint pose to reach the semi-transparent target pose. As the secondary task, a virtual animation will start with different durations and locations. The right figure illustrates the possible locations of the red ball, named Sparse, Median, and Dense, accordingly.
• Figure 2: Noticeability results of the formative study in every condition. The error bars represent the standard errors.
• Figure 3: Psychometric functions of the noticeability in each condition.
• Figure 4: The regression results between gaze distance, saccade frequency, fixation frequency, IPA, and noticeability are presented. The correlation coefficients are indicated in the top right corner.
• Figure 5: The possible locations of the visual stimuli in the data collection study. The locations are divided into three conditions: Central (5 degrees), Near Peripheral (30 degrees), and Mid Peripheral (60 degrees), based on the angular distance to the user's head direction.