[论文解读] The Eye-Head Mover Spectrum: Modelling Individual and Population Head Movement Tendencies in Virtual Reality

Jinghui Hu, Ludwig Sidenmark+2 more|arXiv (Cornell University)|Feb 5, 2026

Gaze Tracking and Assistive Technology被引用 0

一句话总结

论文提出一个连续的眼-头部移动谱来建模VR中个体头部对凝视转移的贡献，展示群体层面的变异性，并在多任务中验证软铰链模型。

ABSTRACT

People differ in how much they move their head versus their eyes when shifting gaze, yet such tendencies remain largely unexplored in HCI. We introduce head movement tendencies as a fundamental dimension of individual difference in VR and provide a quantitative account of their population-level distribution. Using a 360° video free-viewing dataset (N=87), we model head contributions to gaze shifts with a hinge-based parametric function, revealing a spectrum of strategies from eye-movers to head-movers. We then conduct a user study (N=28) combining 360° video viewing with a short controlled task using gaze targets. While parameter values differ across tasks, individuals show partial alignment in their relative positions within the population, indicating that tendencies are meaningful but shaped by context. Our findings establish head movement tendencies as an important concept for VR and highlight implications for adaptive systems such as foveated rendering, viewport alignment, and multi-user experience design.

研究动机与目标

• Characterize how individuals differ in head contribution to gaze shifts in VR.
• Provide a continuous, interpretable per-user profile of head movement tendencies.
• Quantify population-level distribution of head movement strategies using a large open dataset.
• Assess cross-task stability of head contribution tendencies between free viewing and controlled tasks.

提出的方法

• Analyze a large 360° free-viewing VR dataset (N=87; later filtered to N=80 monoscopic) with head and gaze tracking.
• Define horizontal head contribution to gaze shifts as a function of target eccentricity within [0, 50] degrees.
• Fit three parametric models (linear baseline, hinge, soft hinge) to individual data and compare using R^2, RMSE, and AIC.
• Select the soft hinge model as the preferred per-participant profile based on fit quality and interpretability.
• Apply functional PCA to obtain a population distribution of eye-head strategies from participant profiles.

Figure 1. Example fits of three model formulations to one participant (P05). Each plot shows horizontal head contribution ( $\Delta$ Head) against target eccentricity, with grey dots as data points. (a) Linear + EOR baseline assumes a fixed eye-only range followed by a linear increase. (b) Hinge mod

实验结果

研究问题

• RQ1 RQ1: How does head contribution to gaze shifts vary between individuals?
• RQ2 RQ2: How prevalent are different head-movement tendencies in the population?
• RQ3 RQ3: Are individual head-contribution tendencies consistent across tasks?

主要发现

• Head contribution to gaze shifts increases with target eccentricity, forming a spectrum from eye-dominant to head-dominant strategies.
• The soft hinge model explains more variance and achieves lower RMSE than the hinge and linear models, making it the preferred per-participant fit.
• The population exhibits a continuous distribution of head-movement tendencies rather than discrete head movers/non-head movers.
• Tendencies show partial alignment across tasks, suggesting context shapes but does not fully override intrinsic coordination.
• Across participants, head movement patterns are strongly symmetric left–right, supporting data mirroring for analysis.
• The approach provides interpretable per-user profiles (beta, tau, s) that can inform VR system adaptations like foveated rendering and viewport alignment.

Figure 2. Comparison of model fits for gaze shift data. Boxplots show the distribution of R² (left) and RMSE (right) across participants for three models: Linear baseline, two-parameter hinge, and three-parameter soft hinge. Boxes represent the interquartile range (IQR) with medians; black dots indi