Above-Screen Fingertip Tracking and Hand Representation for Precise Touch Input with a Phone in Virtual Reality
Why this work is in the frame
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Bibliographic record
Abstract
Interacting with the touchscreen of a mobile phone in virtual reality (VR) is challenging because users cannot see their fingers when aiming for targets. We propose using two mirrors reflecting the front camera of the phone and a purpose-built deep neural network to infer the 3D position of fingertips above the screen. Network training is self-supervised after only a few hundred initial labelled images and does not require any external sensor. The inferred fingertip positions can be used to control different hand models and objects in VR. Controlled experiments evaluate tracking performance for single-finger touch input, and compare several 3D hand representations with a flat 2D overlay used in previous work. The results confirm the suitability of our fingertip tracker to aid precise tapping of small targets on the phone screen and provide insights about the effect of various hand representations on control and presence. Finally, we provide several application examples showing how 3D fingertip input can complement and extend phone-based touch interaction in VR.
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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 | 0.001 | 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 | 0.001 |
| 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