Robotic Characterization of Markerless Hand-Tracking on Meta Quest Pro and Quest 3 Virtual Reality Headsets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Markerless hand-tracking has become increasingly common on commercially available virtual and mixed reality headsets to improve the naturalness of interaction and immersivity of virtual environments. However, there has been limited examination of the performance of markerless hand-tracking on commercial head-mounted displays. Here, we propose an evaluation methodology that leverages a robotic manipulator to measure the positional accuracy, jitter, and latency of such systems and provides a standardized characterization framework of markerless hand-tracking. We apply this methodology to evaluate the hand-tracking performance of two recent mixed reality devices from Meta: the Quest Pro and Quest 3. Results demonstrate the influence of proximity to the headset, rotation of hand, and joint selected as the tracking feature on hand-tracking performance. We found that hand-tracking error and jitter were lowest for both headsets in conditions where the knuckle was the tracking point compared to the fingertip. Regarding positional accuracy, in best-performing conditions, the Quest Pro outperformed the Quest 3 with 1.22 cm of average error compared to 1.73 cm. The opposite result was true concerning jitter, with results of 1.77 cm and 1.11 cm for the Quest Pro and Quest 3, respectively. We found latency highly variable for the Quest Pro (15.8 - 229.2 ms) and Quest 3 (14.4 - 220.5 ms). This work provides a testing framework for highly systematic and repeatable performance measurements of markerless hand-tracking systems embedded in headsets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it