HeadShift: Head Pointing with Dynamic Control-Display Gain
Bibliographic record
Abstract
Head pointing is widely used for hands-free input in head-mounted displays (HMDs). The primary role of head movement in an HMD is to control the viewport based on absolute mapping of head rotation to the 3D environment. Head pointing is conventionally supported by the same 1:1 mapping of input with a cursor fixed in the centre of the view, but this requires exaggerated head movement and limits input granularity. In this work, we propose to adopt dynamic gain to improve ergonomics and precision, and introduce the HeadShift technique. The design of HeadShift is grounded in natural eye-head coordination to manage control of the viewport and the cursor at different speeds. We evaluated HeadShift in a Fitts’ Law experiment and on three different applications in VR, finding the technique to reduce error rate and effort. The findings are significant as they show that gain can be adopted effectively for head pointing while ensuring that the cursor is maintained within a comfortable eye-in-head viewing range.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".