Exploring Bi-Manual Teleportation in Virtual Reality

TL;DR

This study addresses the gap in VR locomotion research by evaluating bi-manual teleportation and user posture, applying a tailored Fitts' law model to predict performance. It compares uni-manual, bi-manual, and dwell-based techniques using a within-subjects design with $N=20$ participants and a range of target distances, elevations, and sizes. A novel Fitts' law variation MT = $a + b_1\log_2\left(\frac{A}{W}+1\right) - b_2\log_2\left(\frac{W}{\max(D,H)}+1\right)$ is proposed and shown to fit teleportation data better than standard models, as evidenced by higher Adj $R^2$ and lower AIC/BIC. The results show bi-manual input, especially when the dominant hand points and the non-dominant hand confirms (RPLG), enables faster and more accurate teleportation than uni-manual or dwell-based methods, with posture having no significant effect, informing practical design guidelines for VR teleportation systems.

Abstract

Teleportation, a widely-used locomotion technique in Virtual Reality (VR), allows instantaneous movement within VR environments. Enhanced hand tracking in modern VR headsets has popularized hands-only teleportation methods, which eliminate the need for physical controllers. However, these techniques have not fully explored the potential of bi-manual input, where each hand plays a distinct role in teleportation: one controls the teleportation point and the other confirms selections. Additionally, the influence of users' posture, whether sitting or standing, on these techniques remains unexplored. Furthermore, previous teleportation evaluations lacked assessments based on established human motor models such as Fitts' Law. To address these gaps, we conducted a user study (N=20) to evaluate bi-manual pointing performance in VR teleportation tasks, considering both sitting and standing postures. We proposed a variation of the Fitts' Law model to accurately assess users' teleportation performance. We designed and evaluated various bi-manual teleportation techniques, comparing them to uni-manual and dwell-based techniques. Results showed that bi-manual techniques, particularly when the dominant hand is used for pointing and the non-dominant hand for selection, enable faster teleportation compared to other methods. Furthermore, bi-manual and dwell techniques proved significantly more accurate than uni-manual teleportation. Moreover, our proposed Fitts' Law variation more accurately predicted users' teleportation performance compared to existing models. Finally, we developed a set of guidelines for designers to enhance VR teleportation experiences and optimize user interactions.

Figures (4)

• Figure 1: Right Pointer, Right Gesture (RPRG). (a) A user controls the teleportation parabola using the right hand, and (b) selection is done by closing the index finger of the same hand.
• Figure 6: (a) At the beginning of the task, participants were instructed to point to a blue cube and confirm a selection, ensuring that each trial began from a fixed position. (b) Once the blue cube was selected, participants could move the teleportation pointer to a destination using arm and wrist movements.
• Figure 7: (a) Movement time by Technique for each Height, (b) Error rate by Technique for each Target Width. Error bars represent 95% Confidence Interval.
• Figure 8: Subjective feedback on each Technique (out of 100) scores for (a) Mean Effort (the lower the value, the less the effort), (b) Mean Preference (the higher the value, the more preferred the technique). Error bars represent 95% Confidence Interval