Low-Fi VR Controller: Improved Mobile Virtual Reality Interaction via Camera-Based Tracking
Bibliographic record
Abstract
Mobile virtual reality (VR) provides an accessible alternative to high-end VR, but currently offers only limited interaction, hindering its usability, the variety of VR experiences it can provide, and widespread adoption. To improve interaction on mobile VR, we present a novel input solution: the Low-Fi VR Controller. The controller uses the smartphone camera to track markers to provide 6DOF input. It costs virtually nothing as it is made of cost-effective accessible materials. We performed a user study based on Fitts’ law to evaluate the controller's performance in selection tasks, and to compare three selection activation methods (Instant, Dwell, Marker). Despite tracking issues, selection throughput with the Instant method was comparable to other similar ray-based selection techniques reported in other studies, at roughly 2.2 bps. Our results validate the controller as an acceptable 3D input device and will propose avenues to improve performance and user experience with the controller in future work.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".