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arxiv: 2508.12268 · v2 · submitted 2025-08-17 · 💻 cs.HC · cs.CV

iTrace: Click-Based Gaze Visualization on the Apple Vision Pro

Esra Mehmedova , Santiago Berrezueta-Guzman , Stefan Wagner This is my paper

Pith reviewed 2026-05-18 22:17 UTC · model grok-4.3

classification 💻 cs.HC cs.CV
keywords Apple Vision Progaze visualizationclick-based trackingattention heatmapsXR eye trackingprivacy restrictionsdynamic visualization
0
0 comments X

The pith

Click-based proxies let researchers build dynamic gaze heatmaps on the Apple Vision Pro despite blocked continuous eye data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces iTrace as a way to record where users look on the Apple Vision Pro by turning pinch gestures, dwell times, or controller presses into gaze points that are turned into live heatmaps. This bypasses privacy limits that stop direct access to raw eye-tracking streams while still producing usable maps of attention during video watching or spatial tasks. Tests with twenty participants showed a gaming controller collects points over thirty times faster than dwell waiting, yielding denser maps that highlight tight focus in lectures and wider searches during problem solving. The work keeps reported precision at 91 percent and points to uses in education, design review, marketing, and clinical checks.

Core claim

iTrace captures gaze coordinates on the Apple Vision Pro through manual pinch gestures, automatic dwell selection, or gaming controller inputs and converts them into individual and averaged dynamic heatmaps for video and spatial eye tracking. User studies with two groups of ten participants each measured the 8BitDo controller at 14.22 clicks per second versus 0.45 clicks per second for dwell control, producing denser visualizations that show concentrated attention in lecture videos and broader scanning during problem-solving tasks while reporting 91 percent gaze precision.

What carries the argument

Click-based gaze extraction techniques that record user inputs as proxy points and render them as dynamic individual or averaged heatmaps.

Load-bearing premise

Clicks and controller inputs accurately and consistently stand in for the user's actual gaze location on the device.

What would settle it

A side-by-side test that logs click points against the device's internal eye-tracking output in research mode to measure real deviation from the claimed 91 percent precision.

Figures

Figures reproduced from arXiv: 2508.12268 by Esra Mehmedova, Santiago Berrezueta-Guzman, Stefan Wagner.

Figure 1
Figure 1. Figure 1: The iTrace pipeline for click-based gaze mapping on the Apple Vision Pro—(left) video eye tracking: the Swift app captures and sends the gaze data to the server to produce heatmap videos; (right) spatial eye tracking: the application triggers environment recording and gaze capture, then the server overlays heatmaps on the mirrored footage. Abstract The Apple Vision Pro is equipped with accurate eye-trackin… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of click-based interaction methods: [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Clicking speed assessment interface: users tap the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Precision calibration interface: users tap the center [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: Video eye tracking interface: (left) gaze collection [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Spatial eye tracking interface: (left) spatial gaze col [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average precision score 10 participants who watched them with dwell control. The gaming controller gaze collection method produced over 30 times more clicks than dwell control across the lecture and quiz videos. This highlights the big difference in data collection frequency between the two interaction techniques. The higher number of clicks in the quiz video can be explained by its longer duration, which … view at source ↗
Figure 9
Figure 9. Figure 9: Average clicks per second 5.2.3 Average Inner Click Interval. To further evaluate the fre￾quency of the gaze data, the average inner click interval is shown in [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Averaged heatmap frame from the lecture video [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: Average inner click interval for (left) dwell control [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 13
Figure 13. Figure 13: Averaged heatmap frame from the quiz video ob [PITH_FULL_IMAGE:figures/full_fig_p009_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Final heatmap frame of the averaged dwell control [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗
read the original abstract

The Apple Vision Pro is equipped with accurate eye-tracking capabilities, yet the privacy restrictions on the device prevent direct access to continuous user gaze data. This study introduces iTrace, a novel application that overcomes these limitations through click-based gaze extraction techniques, including manual methods like a pinch gesture, and automatic approaches utilizing dwell control or a gaming controller. We developed a system with a client-server architecture that captures the gaze coordinates and transforms them into dynamic heatmaps for video and spatial eye tracking. The system can generate individual and averaged heatmaps, enabling analysis of personal and collective attention patterns. To demonstrate its effectiveness and evaluate the usability and performance, a study was conducted with two groups of 10 participants, each testing different clicking methods. The 8BitDo controller achieved higher average data collection rates at 14.22 clicks/s compared to 0.45 clicks/s with dwell control, enabling significantly denser heatmap visualizations. The resulting heatmaps reveal distinct attention patterns, including concentrated focus in lecture videos and broader scanning during problem-solving tasks. By allowing dynamic attention visualization while maintaining a high gaze precision of 91 %, iTrace demonstrates strong potential for a wide range of applications in educational content engagement, environmental design evaluation, marketing analysis, and clinical cognitive assessment. Despite the current gaze data restrictions on the Apple Vision Pro, we encourage developers to use iTrace only in research settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces iTrace, a client-server system for click-based gaze data capture and heatmap visualization on the Apple Vision Pro to bypass privacy restrictions on continuous eye tracking. It evaluates manual (pinch), dwell, and controller-based input methods through a study with 20 participants, reporting higher data rates for the 8BitDo controller (14.22 clicks/s) versus dwell control (0.45 clicks/s), and claims 91% gaze precision with potential applications in education, design, marketing, and clinical assessment.

Significance. If the precision and proxy-validity claims are substantiated, iTrace offers a practical workaround for dynamic attention visualization on privacy-restricted devices, backed by concrete empirical metrics from a 20-participant study showing clear differences in data density between input methods. This could support attention analysis in educational and design contexts where direct gaze access is unavailable.

major comments (2)
  1. Abstract: the claim of maintaining 'a high gaze precision of 91%' is presented without any description of the measurement protocol, including validation trials, angular error thresholds, ground-truth comparison method, data exclusion rules, or statistical tests, despite the explicit statement that privacy restrictions block continuous gaze access; this directly undercuts the central effectiveness claim and the asserted applicability to education, design, marketing, and clinical assessment.
  2. User study description: the assumption that controller or dwell clicks serve as faithful proxies for actual gaze location is load-bearing for all reported heatmap patterns and precision figures, yet no details are provided on how this proxy was validated (e.g., calibration phase, fixation target comparisons, or consistency checks across participants).
minor comments (1)
  1. Abstract: the two groups of 10 participants are mentioned without clarifying whether tasks, demographics, or counterbalancing were matched across the controller and dwell conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and have revised the manuscript to provide the requested clarifications on measurement protocols and proxy validation.

read point-by-point responses

  1. Referee: Abstract: the claim of maintaining 'a high gaze precision of 91%' is presented without any description of the measurement protocol, including validation trials, angular error thresholds, ground-truth comparison method, data exclusion rules, or statistical tests, despite the explicit statement that privacy restrictions block continuous gaze access; this directly undercuts the central effectiveness claim and the asserted applicability to education, design, marketing, and clinical assessment.

    Authors: We agree that the abstract and main text require additional detail on the precision measurement to substantiate the claim. The 91% figure derives from discrete samples collected during the user study, where click positions were compared to known on-screen fixation targets in a calibration task. In the revised manuscript we have expanded the abstract to note the validation approach and added a new subsection in Methods that specifies the protocol: five validation trials per participant per method, angular error computed relative to target centers, exclusion of trials exceeding a 3-second response latency, and use of paired t-tests to confirm consistency (p < 0.05). This discrete-sample approach respects the privacy constraints while still allowing quantitative assessment of proxy accuracy. revision: yes

  2. Referee: User study description: the assumption that controller or dwell clicks serve as faithful proxies for actual gaze location is load-bearing for all reported heatmap patterns and precision figures, yet no details are provided on how this proxy was validated (e.g., calibration phase, fixation target comparisons, or consistency checks across participants).

    Authors: We acknowledge that explicit validation of the click-as-gaze proxy is essential. The study incorporated a calibration phase in which participants were instructed to fixate on a sequence of on-screen targets and then issue a click (via pinch, dwell, or controller). Click coordinates were then compared to target locations to quantify spatial agreement. We have added these details to the User Study section, including the number of calibration repetitions per participant, the observed consistency across the 20 participants, and the decision criteria for accepting a method as a valid proxy. These additions directly support the reported heatmap patterns and precision metric. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with independent click-rate measurements

full rationale

The paper presents a system description and reports results from a user study with 20 participants comparing two clicking methods (8BitDo controller at 14.22 clicks/s vs. dwell control at 0.45 clicks/s). The 91% gaze precision figure is stated as an outcome of the evaluation but is not derived from any equations, fitted parameters, or self-citations that reduce to the input data by construction. No mathematical derivation chain exists; all load-bearing claims rest on direct empirical measurements and participant testing rather than self-referential definitions or renamings. The work is therefore self-contained against external benchmarks of usability and data-collection rates.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

This is an applied systems and HCI paper whose central claim rests on the domain assumption that discrete user inputs can serve as reliable gaze proxies and on the practical usability of the implemented client-server pipeline; no mathematical free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption User clicks, dwell times, or controller inputs can be mapped to gaze coordinates with sufficient accuracy to produce meaningful attention heatmaps.
    This premise is required for the click-based extraction techniques to substitute for direct gaze data.
invented entities (1)
  • iTrace system no independent evidence
    purpose: Client-server application that captures click-based gaze proxies and generates dynamic heatmaps on Apple Vision Pro
    The system is newly developed and described in this work to overcome device-specific privacy restrictions.

pith-pipeline@v0.9.0 · 5781 in / 1372 out tokens · 47495 ms · 2026-05-18T22:17:53.467424+00:00 · methodology

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Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · 1 internal anchor

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