Exploring Pointing and Confirmation Techniques for Teleportation Across Varying Elevations in Virtual Reality
Bibliographic record
Abstract
Teleportation in Virtual Reality (VR) is a locomotion technique that allows users to navigate between locations within a virtual environment instantly. Traditionally, VR teleportation is performed using physical controllers, where users control a teleportation pointer — represented by a straight line or parabola — and activate the teleportation to the target destination by pressing a button. Recent advances in hand and eye-tracking capabilities in Head-Mounted Displays (HMDs) enable designers to leverage hand and eye-based interactions to enhance the immersion and naturalness of controller-free VR usage. However, there has been limited research on comparing different controller-free methods for VR teleportation across various elevations. To address this gap, we conducted a user study exploring three controller-free pointing techniques (gaze, hand, and head), four confirmation modalities (finger pinch, eye-blink, dwell, and voice), and two types of teleportation pointers (linear and parabolic) for VR teleportation across various elevations. Our results show that head-based pointing was faster and more accurate than other techniques, with head and gaze achieving higher throughput than hand-based methods. For confirmation, finger pinch yielded the best performance in terms of task completion time and throughput, followed by dwell, voice, and eye-blink; dwell was the most accurate. The linear pointer outperformed the parabolic pointer in some contexts. Based on these findings, we propose design guidelines to enhance controller-free VR teleportation using various input modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".