SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring

Aayush Atul Verma; Arpitsinh Vaghela; Bharatesh Chakravarthi; Kaustav Chanda; Yezhou Yang

REVIEW 3 major objections 5 minor 33 references

SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper introduces SEPose, a synthetic event-based pedestrian pose dataset, and argues that models trained on it generalize to real event-camera data well enough to support traffic-safety monitoring.

desk verdict The dataset fills a real gap, but the paper's pose-generalization evidence is box-level detection and the abstract's pedestrian count is ~16x too high. read the letter →

arxiv 2507.11910 v1 pith:HPHKFI4K submitted 2025-07-16 cs.CV

Kaustav Chanda , Aayush Atul Verma , Arpitsinh Vaghela , Yezhou Yang , Bharatesh Chakravarthi This is my paper

classification cs.CV

keywords eventcamerahumanposeestimationsyntheticdatasetpedestrianmonitoringsim-to-realtransferCARLAsimulatordynamicvisionsensorintelligenttransportation

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

The paper introduces SEPose, a synthetic event-based human pose estimation dataset built for fixed traffic-camera pedestrian monitoring, generated in the CARLA simulator with a dynamic vision sensor. It claims that the dataset's roughly 73K event frames and 350K annotated pedestrians, spanning diverse weather, lighting, and crowd densities across urban, suburban, and rural intersections, provide enough structure for models to generalize to real event-camera data. To test this, the authors train RVT and YOLOv8 on SEPose and evaluate on the real PEDRo and DHP19 datasets. They report substantial absolute performance and argue that the transfer shows synthetic training produces meaningful pose features for real data. If correct, this gives the event-vision community a low-cost, diverse source of pedestrian pose data for safety-critical monitoring.

What carries the argument

The central object is the dataset itself, generated by a pipeline that pairs CARLA's RGB cameras with a simulated dynamic vision sensor operating synchronously at 33ms steps. Weather parameters are incremented every 100 frames to cover rain, fog, and day-night cycles, and pedestrian spawn counts are sampled from a Gaussian with mean 50 and standard deviation 15 to vary crowd density. A ray-cast occlusion filter drops pedestrians whose keypoints are more than half hidden, and a 300-pixel bounding-box minimum removes distant figures. For evaluation, pose annotations from SEPose, PEDRo, and DHP19 are converted to bounding boxes and scored with mAP and mAP50 over IoU thresholds; this box-level protocol is the bridge between the synthetic training set and real test sets.

What would settle it

Recompute the same transfer experiment using keypoint-level metrics such as OKS or PCK on PEDRo and DHP19 rather than bounding boxes derived from keypoints. If the models' bounding-box mAP is high but per-keypoint localization error is close to chance, or clearly worse than a model trained on real event data, the paper's claim of meaningful pose-feature transfer is unsupported.

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Extended reading notes

Core claim

The paper's central claim is that a large synthetic event-based dataset can supply the missing training data for pedestrian pose estimation from fixed traffic cameras. SEPose contains about 73K event frames with 350K annotated pedestrians, captured by a simulated dynamic vision sensor with 800x600 resolution and 90-degree field of view, across six CARLA maps and systematically varied weather and lighting. Annotations follow the COCO keypoint format with 16 joints, and each annotation entry is filtered to remove pedestrians with more than 50% occluded keypoints or bounding boxes smaller than 300 pixels in area. Training RVT and YOLOv8 on SEPose and evaluating on the real PEDRo and DHP19 datasets gives, respectively, mAP/AP50 of 69.9/98.1 and 63.2/94.7 on SEPose, dropping to 44.4/53.9 and 39.4/55.1 on PEDRo, and 51.1/61.3 and 53.7/58.9 on DHP19. The paper interprets these nonzero transfer numbers as evidence that models trained on synthetic data learn meaningful pose features for real event data.

Load-bearing premise

The evaluation assumes that bounding boxes generated from pose keypoints are a valid measure of pose estimation performance, so the reported sim-to-real evidence depends on box-level detection reflecting keypoint localization quality.

Editorial extensions

If this is right

Event-based pose models can be pre-trained on synthetic traffic scenes, reducing the need for expensive manual annotation of real event-camera pedestrian data.
The variety of side and three-quarter pedestrian views, unlike centered indoor datasets, gives models exposure to viewpoints typical of fixed traffic cameras.
Because annotations include 16 body keypoints, downstream tasks such as gait-direction prediction and collision-risk estimation become trainable from the same data.
The measured sim-to-real gap on PEDRo and DHP19 provides a baseline for future work on event-camera noise simulation and domain adaptation.

Reading between the lines

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

A stricter test would evaluate keypoint-level agreement (for example, OKS or PCK) directly on PEDRo and DHP19, since the paper's bounding-box mAP converts poses to boxes and may not isolate keypoint localization quality.
If the sim-to-real gap is mostly due to event-camera noise and background statistics, then adding synthetic noise models or small amounts of real unlabeled event data during training could substantially close the reported drop.
Combining SEPose with the existing real datasets in mixed training could yield a model that keeps SEPose's viewpoint diversity while anchoring to real event statistics, a setup the paper does not test.
A direct real-to-synthetic evaluation (training on PEDRo/DHP19 and testing on SEPose) would clarify which aspects of the gap come from the synthetic sensor model versus the domain shift in scenes.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

The headline: SEPose is a genuinely useful new resource—the first large synthetic event-based pedestrian pose dataset for fixed traffic cameras—but the paper's evidence for sim-to-real pose generalization is currently box-level detection, not pose estimation, and the instance count in the abstract is off by about 16x.

What's new and good: CARLA is used sensibly, with fixed traffic-camera placements at 10m and 2m, varied maps, weather sweeping, Gaussian crowd density, and a ray-cast occlusion filter that removes pedestrians with >50% keypoints occluded. The dataset format follows COCO keypoints with 16 joints, and the pipeline is described in enough detail to reproduce. Few published resources do this for event-based traffic scenes; DHP19 is indoor and PEDRo is detection-only, so the gap is real.

Soft spots: the experiments don't test pose estimation. RVT with a YOLO head and YOLOX are object detectors; the paper generates bounding boxes from keypoint annotations and computes mAP. No keypoint-level metric (OKS, PCK, keypoint AP) is reported on any split, including SEPose itself. So the abstract's claim that training on SEPose demonstrates 'sim-to-real generalization capabilities' is unsupported for the pose task the dataset is named for. The conclusion that models 'learn meaningful features required for accurate predictions on real data' is an overstatement given the large accuracy drops—e.g., RVT mAP drops from 69.9 to 44.4 on PEDRo.

Second, the numbers don't line up: the abstract says 'nearly 350K annotated pedestrians,' but Section III-D says 'over 350K human pose keypoints.' With 16 keypoints per pedestrian, those differ by a factor of 16. The text should say what it actually is.

Third, the dataset is not publicly accessible from the paper—no URL, no release statement. For a dataset paper, that's a real deficiency; the community can't verify or use the contribution.

Minor: abstract says YOLOv8, experiments use YOLOX; should be aligned.

These are fixable. The generation pipeline is solid and the dataset itself is the contribution; the evaluation can be redone or the claims reframed as detection transfer. The paper deserves a serious referee, but it needs major revision before acceptance.

Recommendation: send to peer review, but require keypoint-level evaluation on held-out SEPose and on DHP19's projected 2D keypoints, correct the counts, and make the data available.

Regards.

Referee Report

3 major / 5 minor

Summary. The paper introduces SEPose, a synthetic event-based multi-person human pose estimation dataset for fixed traffic-camera pedestrian monitoring, generated with the CARLA simulator. The dataset comprises about 73K event frames, corresponding RGB frames and raw event streams, and 16 COCO-style 2D body keypoints per annotated pedestrian, with diversity across six CARLA maps, weather conditions, time-of-day, crowd densities, and camera elevations. The authors describe the generation and cleaning pipeline, including weather parameter sweeps, collision-triggered resets, ray-cast occlusion filtering, and a minimum bounding-box area filter. They then train RVT with a YOLO detection head and YOLOX on SEPose and evaluate on the real event-based datasets PEDRo and DHP19, reporting mAP and AP50. The central claim is that training on SEPose demonstrates sim-to-real generalization for event-based human pose estimation. My assessment is that the dataset creation is described in valuable detail, but the experimental evidence does not currently support the pose-estimation generalization claim because only box-level detection metrics are reported.

Significance. If the pose-generalization claim were supported, SEPose would fill a clear gap: a publicly described, large-scale synthetic event-based dataset with pedestrian pose keypoints for ITS-oriented, fixed-camera monitoring, together with a detailed generation recipe that others could replicate in CARLA. The paper's strengths are its concrete pipeline description (sensor configuration, weather sweeps, occlusion filtering via ray casting, bounding-box filtering, Gaussian crowd-density sampling) and the choice of diverse maps and camera placements. The dataset contribution is potentially useful regardless of the transfer results. However, the claimed sim-to-real generalization for human pose estimation is not supported by the reported experiments, which evaluate only person/box detection and do not report any keypoint-level metric on synthetic or real data. The paper also contains a numerical inconsistency between the abstract and Section III-D concerning the dataset size, which must be resolved.

major comments (3)

[Abstract and Section IV] The central claim of sim-to-real generalization for human pose estimation is not supported by the reported experiments. Section IV states: 'we generate bounding boxes from all pose annotations and compute the mean average precision and mAP50 scores for the corresponding IoU scores.' The two models evaluated, RVT with a YOLO detection head and YOLOX, are object detectors that output bounding boxes, not keypoints. No keypoint-level metric (OKS, PCK, or keypoint AP) is reported on any split, and no held-out synthetic SEPose evaluation is presented. Because PEDRo only has bounding-box annotations, and DHP19's projected 2D keypoints are used only to form boxes, the reported 69.9 mAP on PEDRo and 51.1 mAP on DHP19 demonstrate detection transfer, not pose transfer. The authors must either add a proper keypoint evaluation on DHP19, add an in-domain held-out pose evaluation on SEPose, or explicitly revise the claims in the abstract and conclusion to describe detection-level transfer.
[Section III-D and Abstract] There is a factor-of-16 discrepancy in dataset size reporting. The abstract says 'nearly 350K annotated pedestrians,' while Section III-D says the dataset contains 'over 350K human pose keypoints.' Since each pedestrian is annotated with 16 keypoints, 350K keypoints corresponds to roughly 22K pedestrian instances, not 350K pedestrians. The authors should correct the abstract, Section III-D, and any other occurrences, and state clearly whether the intended quantity is the number of pedestrian instances or the total number of keypoints.
[Section IV] The absence of any in-domain evaluation on SEPose itself weakens the empirical claims. The only numerical results are on PEDRo and DHP19, so the reader cannot distinguish a sim-to-real domain gap from a model's failure to learn pose structure from SEPose. At minimum, the paper should report keypoint-level performance on a held-out synthetic split (e.g., keypoint AP or PCK), which would establish that models trained on SEPose actually learn the annotated pose task before the transfer question is addressed.

minor comments (5)

[Section IV] The text says YOLO8X is trained on event frames generated 'with a time delta of 0.33 ms,' which appears to be a typographical error: the event accumulation time and simulation step described in Section III-A are 33 ms. Please correct the unit or value.
[Abstract and Section IV] The model naming is inconsistent: the abstract refers to YOLOv8, while Table III and the text use 'YOLO8X' and 'Yolo8X,' and Reference [34] is actually YOLOX. Please standardize the model name and ensure the cited paper matches the evaluated architecture.
[Section IV] The discussion says PEDRo 'includes more diverse indoor as well as outdoor scenarios,' but Section II describes PEDRo as an outdoor event-based dataset recorded with a handheld camera. These descriptions are inconsistent and should be reconciled.
[Section III-C] Reference [25] is cited for the COCO keypoint annotation format, but that reference is a pose-estimation paper, not the COCO dataset or keypoint challenge definition. Please cite the original COCO keypoint annotation source.
[General] The paper would benefit from a short statistics table stating the total number of annotated pedestrian instances, the average number of keypoints per instance, the number of frames, and the split into maps and weather conditions; this would also resolve the quantity mismatch noted above.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the sim-to-real evaluation is an external benchmark transfer, and self-citations are not load-bearing.

full rationale

The paper contains no derivation in which a predicted quantity is defined in terms of the same quantity it is meant to predict. The central sim-to-real experiment trains RVT and YOLOX on SEPose and evaluates them on the real event-based datasets PEDRo and DHP19, which are external benchmarks not generated or fitted by this work. The evaluation converts pose annotations into bounding boxes to compute mAP scores; this may weaken the pose-specific interpretation of the results, but it is a choice of external benchmark protocol, not a self-referential loop. The self-citations in the reference list (e.g., the authors' earlier surveys and synthetic traffic datasets) appear as related work and contextual support only; none of them is invoked to justify the dataset's generalization claim. There is no fitted parameter later relabeled as a prediction, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation. The skeptical concern that box-level detection does not demonstrate keypoint-level pose generalization is a validity or correctness issue, not circularity. Accordingly, the paper is self-contained against external benchmarks and receives a score of 0.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper makes no analytic derivation; its central claim rests on the fidelity of CARLA's event and pose simulation and on the validity of cross-dataset evaluation. The hand-chosen thresholds and distributions listed above determine what the dataset contains.

free parameters (6)

DVS contrast threshold C = 0.3
Set for both event polarities in the CARLA DVS sensor (Section III-A). Controls event density; chosen by hand rather than calibrated to a real sensor.
Event accumulation time and simulation step = 33ms
Used to generate event frames and run the simulation in synchronous mode (Section III-A). A design choice that determines temporal resolution.
Pedestrian spawn distribution = Gaussian mean 50, std 15
Number of pedestrians to spawn is sampled from this distribution to vary crowd density (Section III-C).
Weather parameter increment = 20% every 100 frames
Weather parameters are sequentially incremented to cover diverse conditions (Section III-B).
Minimum pedestrian bounding box area = 300 px^2
Pedestrians with bounding boxes smaller than this area are excluded from annotations (Section III-B).
Occlusion removal threshold = 50% of keypoints
Pedestrians with more than half of keypoints occluded are removed from ground truth (Section III-B).

assumptions (4)

domain assumption CARLA's DVS event camera simulation is faithful enough to real event cameras that models trained on synthetic events transfer to real data.
The sim-to-real experiment is the only check; results show a 27 to 45 percent drop, so this assumption holds only partly, if at all.
domain assumption Bounding boxes derived from 2D keypoints are a sufficient proxy for pose estimation performance across datasets with different joint definitions.
Used to compute mAP and AP50 in Section IV; no keypoint similarity or localization error is measured.
domain assumption CARLA's exported pedestrian skeletons correspond to real anatomical keypoints and project correctly into camera coordinates.
Ground-truth keypoints come from the CARLA skeleton API (Section III-C).
domain assumption The weather and lighting parameter grid yields a distribution representative of real traffic-monitoring conditions.
Weather parameters are incremented by 20 percent every 100 frames (Section III-B); no comparison to real weather statistics is given.

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Cite this review

Pith. "Pith review of SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring." pith.science (2026). https://pith.science/paper/HPHKFI4K

@misc{pith2026250711910,
  author       = {Pith},
  title        = {Pith review of: SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HPHKFI4K}},
  note         = {Machine review of arXiv:2507.11910}
}

read the original abstract

Event-based sensors have emerged as a promising solution for addressing challenging conditions in pedestrian and traffic monitoring systems. Their low-latency and high dynamic range allow for improved response time in safety-critical situations caused by distracted walking or other unusual movements. However, the availability of data covering such scenarios remains limited. To address this gap, we present SEPose -- a comprehensive synthetic event-based human pose estimation dataset for fixed pedestrian perception generated using dynamic vision sensors in the CARLA simulator. With nearly 350K annotated pedestrians with body pose keypoints from the perspective of fixed traffic cameras, SEPose is a comprehensive synthetic multi-person pose estimation dataset that spans busy and light crowds and traffic across diverse lighting and weather conditions in 4-way intersections in urban, suburban, and rural environments. We train existing state-of-the-art models such as RVT and YOLOv8 on our dataset and evaluate them on real event-based data to demonstrate the sim-to-real generalization capabilities of the proposed dataset.

Figures

Figures reproduced from arXiv: 2507.11910 by the authors.

**Figure 2.** Schematic representation of the camera positions in rural (left – Town7), suburban (middle – Town5), and urban [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Dataset samples of images from the RGB sensor (top) and DVS event camera (bottom) with overlaid human pose [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: (left) RGB image and (right) joint keypoints overlaid [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: (left) Histogram of bounding box diagonal size, (right) [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

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Reference graph

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Reviewed August 6, 2026 · model on record in the stance chip above.

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