Adaptive Hand Visibility for Accurate 3D User Interactions in Virtual Environments
Bibliographic record
Abstract
Hand visualization significantly impacts user performance in virtual reality (VR), particularly in tasks requiring precise finger movements. Common hand avatar visualizations, such as opaque or transparent, often occlude critical elements or distract users, potentially reducing accuracy. To address this issue, we investigated adaptive hand visibility techniques, which vary the hand avatar visibility based on the current movement sub-task aiming to enhance user performance. We evaluated these techniques on a VR-based Pedicle Screw Placement task with 15 participants. Each participant inserted virtual screws into a spine using five hand visualization conditions: opaque, transparent, invisible, speed-based visibility (hand visibility changes with hand speed), and position-based visibility (hand visibility adapts to the proximity of critical elements). Results showed that speed-based and position-based visualizations significantly improved accuracy, usability, and overall performance compared to traditional methods. The widely adopted opaque visualization yielded the lowest accuracy and usability. Our findings emphasize the benefits of adaptive hand visualization based on sub-tasks, recommending its implementation in VR applications to increase usability and user accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".