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REVIEW 2 major objections 7 minor 51 references

Event-Based Eye Tracking. 2025 Event-based Vision Workshop

T0 review · 2 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Event-based pupil tracking reaches 1.14-pixel error on a public benchmark, the paper reports.

desk verdict The 2025 leaderboard numbers are a useful descriptive snapshot, but the year-over-year improvement claim is not supported because the metric and label frequency changed; the paper is worth publishing with modest revisions. read the letter →

arxiv 2504.18249 v1 pith:SL7UP6Q5 submitted 2025-04-25 cs.CV cs.AIcs.LG

Qinyu Chen , Chang Gao , Min Liu , Daniele Perrone , Yan Ru Pei , Zuowen Wang , Zhuo Zou , Shihang Tan
show 24 more authors
Tao Han Guorui Lu Zhen Xu Junyuan Ding Ziteng Wang Zongwei Wu Han Han Yuliang Wu Jinze Chen Wei Zhai Yang Cao Zheng-jun Zha Nuwan Bandara Thivya Kandappu Archan Misra Xiaopeng Lin Hongxiang Huang Hongwei Ren Bojun Cheng Hoang M. Truong Vinh-Thuan Ly Huy G. Tran Thuan-Phat Nguyen Tram T. Doan
This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords event-basedvisioneyetrackingpupilcenterlocalizationdynamicsensorchallengesurveypixelerrortemporalmodelingaugmentedreality
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper is the survey of the 2025 event-based eye-tracking challenge, in which teams predicted pupil-center coordinates from event-camera streams on the 3ET+ benchmark. Its central claim is that four compact models, ranging from 0.8 to 7.1 million parameters, all achieve a pixel error below 1.7, with the best at 1.14 pixels, at labels sampled at 100 Hz. The paper argues this improves on the previous year's best result, and it attributes the gains to short- and long-term temporal modeling, data augmentation, and model-agnostic post-processing. A sympathetic reader would care because accurate, low-cost pupil tracking at high temporal resolution is a building block for eye-based interaction in augmented and virtual reality and for non-invasive health monitoring.

What carries the argument

The central objects are the event stream as spatiotemporal tuples $(x, y, t, p)$, the binned event-frame representation, the pixel-error metric defined as Euclidean distance between predicted and ground-truth pupil coordinates, and a set of network designs that combine spatial encoders with temporal sequence models. The winning architecture uses a bidirectional relative-positional attention bias that linearly down-weights attention with temporal distance, separately for forward and backward directions. The post-processing mechanism computes local motion variance to set an adaptive median-filter window and aggregates event flow in a region of interest to shift predictions. These components together carry the argument that sparse event data plus explicit temporal modeling can reach sub-1.7-pixel accuracy at 100 Hz.

What would settle it

Rerun this year's top models on the 2024 evaluation protocol (p-accuracy at a 10-pixel threshold with 20 Hz labels) and compute their p-10 scores; if they do not exceed last year's best p-10 on the same protocol, the claimed improvement is an artifact of the metric and label-rate change.

Watch

Extended reading notes

Core claim

The survey reports that event-based pupil tracking has crossed a practical accuracy threshold: four independent methods localize the pupil center with 1.14 to 1.61 pixel error at 100 Hz on the 3ET+ benchmark while keeping model sizes between 0.8 and 7.1 million parameters. The winning approach combines a convolutional spatial encoder with a bidirectional gated recurrent unit and a self-attention module whose relative position bias is split into forward and backward temporal components. Other top solutions use a 3D CNN cascaded with GRU and Mamba modules to capture implicit short-term and explicit long-term dynamics, or apply inference-time post-processing—motion-aware median filtering and optical-flow-based local refinement—to an existing lightweight spatiotemporal network. The paper further reports that data augmentation such as temporal shifting, spatial flipping, and random event deletion improves accuracy from 1.70 to 1.61 pixel error on the same network, and it frames these results as evidence that event-based eye tracking is viable for low-power, high-speed wearable systems.

Load-bearing premise

The headline 'better than last year' assumes that this year's pixel-error scores at 100 Hz labels are directly comparable with last year's p-accuracy scores at 20 Hz labels, even though the survey states both changed.

Editorial extensions

If this is right

  • At 1.14 to 1.61 pixel error, event-based trackers are within the range needed for gaze-based interaction in head-mounted displays, where sub-2-pixel errors are considered usable.
  • Model-agnostic post-processing that adds only about 512 floating-point operations per prediction can improve any base tracker without retraining, which is a practical recipe for edge deployment.
  • Sub-million-parameter models reach 1.42 to 1.61 pixel error, suggesting that event-based eye tracking is feasible for the power and memory budgets of wearable devices.
  • The shift from p-accuracy to raw pixel error exposes headroom that near-perfect p-10 scores hid, making further algorithmic improvement visible and measurable.
  • Explicitly modeling short-term motion (with recurrent or 3D convolutional layers) and long-term dependencies (with attention or state-space models) is a workable template for event-sequence regression.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the year-over-year comparison is not like-for-like because both the label rate (20 Hz to 100 Hz) and the metric (p-accuracy to pixel error) changed; the real gain may be in temporal resolution rather than in spatial accuracy.
  • Editorial inference: reporting only point estimates of pixel error without variance, confidence intervals, or per-subject breakdowns makes differences among the four top teams (1.14 vs 1.61) hard to interpret; a repeated-seed evaluation could establish whether the ordering is stable.
  • Editorial inference: a natural follow-up is an end-to-end event-driven implementation, since several components (attention, GRU, Mamba) are sequential and the paper's own hardware section names latency, sparsity, and burst handling as the open constraints.
  • Editorial inference: the augmentation recipe (temporal shift, spatial flip, event deletion) is tested on only one base network; applying it across the other top architectures would tell whether the gain is general or architecture-specific.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. The paper is a workshop survey of the 2025 Event-Based Eye Tracking Challenge held at CVPR 2025. It introduces the 3ET+ dataset and the pupil-center regression task, describes the evaluation metric (pixel error) and challenge organization, summarizes the top four team solutions (BRAT, EyeTracking@SMU post-processing, TDTracker, and CherryChums data augmentation), and closes with a hardware-oriented discussion. The central claim is that four teams achieved pixel errors below 1.7, outperforming the best results from the previous year's challenge.

Significance. If taken as reported, the challenge results are a useful descriptive record: compact models (0.8–7.1M parameters) reportedly achieve pixel errors of 1.14–1.61 at 100 Hz on 3ET+. The survey has concrete strengths: it documents the challenge protocol, reports parameter counts and MACs, and mandates source-code submission for reproducibility. However, the headline claim of year-over-year improvement is not supported because the 2025 and 2024 evaluation protocols are not comparable. The survey therefore is a useful archive of the 2025 competition but does not, as written, establish that event-based eye tracking has advanced beyond the previous edition.

major comments (2)
  1. [Section 2.5] The claim that 'four participating teams achieved a pixel error below 1.7, outperforming the best results from the previous year' is not supported by the evidence presented. The manuscript itself states that both the label frequency (20 Hz to 100 Hz) and the evaluation metric (p-accuracy to pixel error) changed. A high p@10 score from 2024 (e.g., 99.37% for CherryChums in Table 7) is not equivalent to a pixel error below or above 1.7, and the 5x increase in label frequency changes the task. No 2024 model was re-evaluated under the 2025 protocol, so the improvement claim is not a like-for-like comparison. This claim is load-bearing for the paper's narrative and should be either removed or supported by a re-evaluation of 2024 baselines under the 2025 metric.
  2. [Table 1 and Section 3] The top-team pixel errors (1.14, 1.42, 1.50, 1.61) are reported as single point values without error bars, standard deviations, or multiple runs. The differences between adjacent ranks are less than half a pixel, which may be within the noise of manual pupil-center annotation or run-to-run variation. Without variance estimates, the ranking itself and the 'notable improvement' claim are not statistically grounded. The survey should report repeated evaluations or at least discuss the expected annotation noise level.
minor comments (7)
  1. [Algorithm 2, line 26] The update for the y-coordinate uses dx in the numerator: 'y_(R,f,pred) <- y_(f,pred) + dx / ||dx,dy||'. This appears to be a typographical error; it should use dy. As written, the pseudocode does not implement the described optical-flow refinement.
  2. [Section 5.1, Eq. (4)] The loss formula is written as (1/T) * sqrt( sum_{t=1}^T (pred - label)^2 ). This is not the standard RMSE, which would be sqrt( (1/T) * sum ). The current expression scales the error down by an additional factor of sqrt(T), which may unintentionally alter the training objective. Please clarify.
  3. [Table 1 and Tables 4, 6] It is not stated whether the 'pixel error' column in Table 1 refers to the private or public leaderboard scores. Teams report both in their own tables; the survey should specify which value is used for the final ranking.
  4. [Section 5.3, Results] The sentence 'After post-processing, the MSE is optimized to 1.4936 on the interpolation ground truth from 3ET+ 2024' is confusing, as the paper elsewhere uses pixel error, and the relationship between this value and the private/public scores is not explained.
  5. [Abstract] The phrase 'methods from teams rank the top' is ungrammatical; it should be 'methods from the top-ranked teams.' Also, 'In each method, accuracy, model size, and number of operations are reported' is awkward and could be rephrased.
  6. [Section 3.2] The statement 'There were, in total, 22 user accounts registered and participated the challenge' is ambiguous: does it mean 22 unique teams or 22 accounts? It should be clarified.
  7. [Section 6] The hardware discussion is general and not tightly connected to the challenge results presented earlier; consider linking it to the specific efficiency figures reported by the teams.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the survey reports an externally run leaderboard. The year-over-year improvement claim is unsupported because the metric and label frequency changed, but that is a correctness/fairness gap, not a reduction of the claim to its inputs.

full rationale

This paper is a challenge survey, not a derivation chain. There is no equation that is fitted to data and then renamed as a prediction, no ansatz imported through self-citation as an external theorem, and no uniqueness claim used to force a choice. The top-team pixel errors in Table 1 are measured Kaggle leaderboard scores, not quantities constructed from the organizers' inputs. The only potentially load-bearing assertion is in Section 2.5: 'four participating teams achieved a pixel error below 1.7, outperforming the best results from the previous year.' That claim is not circular. The paper itself states that both the label frequency (20 Hz to 100 Hz) and the metric (p-accuracy to pixel error) changed, so the comparison is not like-for-like; no re-evaluation of 2024 models under the 2025 protocol is reported, and no variance or annotation-noise estimate is given. These are evidentiary limitations, but the assertion is not equivalent to its own inputs by construction. The self-citations to the organizers' own 3ET+ dataset [11,47] and to baselines such as [10,37] are normal for a challenge summary and are not load-bearing: the leaderboard results are externally generated by participant submissions. The HKUSTGZ report's phrase 'interpolation ground truth from 3ET+ 2024' may indicate that the 100 Hz labels were interpolated rather than independently annotated, which would be a benchmark-validity concern rather than a circularity step. No circular step meeting the evidentiary bar is present, so the analysis is an honest non-finding.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The survey has no derived equations or new physical entities. The only hand-set numeric parameter visible in the text is HKUSTGZ's 0.09 blink ratio. The main implicit inputs are data-quality and reporting-integrity assumptions.

free parameters (1)
  • Blink ratio threshold (HKUSTGZ) = 0.09
    Hand-set threshold in Section 5.3 decides open/closed eye and overwrites predictions; no justification or sensitivity analysis is given.
assumptions (3)
  • domain assumption 3ET+ ground-truth labels and the pixel-error metric are accurate enough to rank methods.
    The whole leaderboard rests on manual 100Hz pupil labels (Sec. 2.1); if labels are noisy, the reported sub-1.7 pixel errors are not meaningful.
  • domain assumption Team-reported parameters, errors, and MAC counts are honest and verified by the mandated code submission.
    Sec. 3.3 says source code submission was mandated, but the survey contains no independent audit of the numbers.
  • domain assumption Event-based eye tracking can be evaluated year-over-year despite metric and label-rate changes.
    Sec. 2.5 changes the metric from p-accuracy at 20Hz to pixel error at 100Hz, so 'outperforming the best results from the previous year' assumes cross-metric comparability.

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Cite this review

Pith. "Pith review of Event-Based Eye Tracking. 2025 Event-based Vision Workshop." pith.science (2026). https://pith.science/paper/SL7UP6Q5

@misc{pith2026250418249,
  author       = {Pith},
  title        = {Pith review of: Event-Based Eye Tracking. 2025 Event-based Vision Workshop},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SL7UP6Q5}},
  note         = {Machine review of arXiv:2504.18249}
}
read the original abstract

This survey serves as a review for the 2025 Event-Based Eye Tracking Challenge organized as part of the 2025 CVPR event-based vision workshop. This challenge focuses on the task of predicting the pupil center by processing event camera recorded eye movement. We review and summarize the innovative methods from teams rank the top in the challenge to advance future event-based eye tracking research. In each method, accuracy, model size, and number of operations are reported. In this survey, we also discuss event-based eye tracking from the perspective of hardware design.

Figures

Figures reproduced from arXiv: 2504.18249 by the authors.

Figure 1
Figure 1. Comparison of the processing flow and estimation patterns between frame-based and event-based systems for eye tracking. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. BRAT network by Team USTCEventGroup. nents as: B i forward = ( mi · (t − s), t ≥ s 0, t < s , (2) B i backward = ( 0, t ≥ s mi · (s − t), t < s , (3) where mi denotes the sensitivity of head i to the relative position, generated via a monotonically decreasing linear mapping to progressively diminish attention to distant steps. Furthermore, to improve the robustness of the model the USTCEventGroup adopted a multi-tim… view at source ↗
Figure 3
Figure 3. Bidirectional Relative Positional Attention. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The architecture of TDTracker. TDTracker primarily comprises two components, Implicit Temporal Dynamic (ITD) and Explicit [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: The visualization heatmap generated by the TDTracker. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: A compact spatiotemporal model integrating data augmentation with spatial and temporal processing blocks. Convolutional [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 8
Figure 8. Figure 8: Overview of the data augmentation techniques used by [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.