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Record W4412445379 · doi:10.1109/tvcg.2025.3589333

Head-EyeK: Head-Eye Coordination and Control Learned in Virtual Reality

2025· article· en· W4412445379 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Visualization and Computer Graphics · 2025
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceVirtual realityHead (geology)Optical head-mounted displayHuman–computer interactionComputer graphics (images)VisualizationArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Human head-eye coordination is a complex behavior, shaped by physiological constraints, psychological context, and gaze intent. Current context-specific gaze models in both psychology and graphics fail to produce plausible head-eye coordination for general patterns of human gaze behavior. In this paper, we: 1) propose and validate an experimental protocol to collect head-eye motion data during sequential look-at tasks in Virtual Reality; 2) identify factors influencing head-eye coordination using this data; and 3) introduce a head-eye coordinated Inverse Kinematic gaze model Head-EyeK that integrates these insights. Our evaluation of Head-EyeK is three-fold: we show the impact of algorithmic parameters on gaze behavior; we show a favorable comparison to prior art both quantitatively against ground-truth data, and qualitatively using a perceptual study; and we show multiple scenarios of complex gaze behavior credibly animated using Head-EyeK.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.323
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it