VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection

Gabriel Maldonado; Ghazal Alinezhad Noghre; Hamed Tabkhi; Narges Rashvand; Shanle Yao

REVIEW 4 major objections 5 minor 26 references

VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Adapting vector-quantized GANs to keypoint sequences yields a discrete motion vocabulary whose reconstruction error flags human behavior anomalies and transfers across datasets without retraining.

desk verdict Useful first step toward discrete motion vocabularies for pose VAD, but the central claim about quantization is not yet isolated from a matched continuous baseline. read the letter →

arxiv 2608.05069 v1 pith:5UVWUVQM submitted 2026-08-05 cs.CV cs.AI

Narges Rashvand , Ghazal Alinezhad Noghre , Shanle Yao , Gabriel Maldonado , Hamed Tabkhi This is my paper · ORCID

classification cs.CVcs.AI

keywords videoanomalydetectionpose-basedvectorquantizationmotioncodebookkeypointsequencesreconstructionerrorcross-domaingeneralizationhumanrepresentation

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

VQ-VAD claims that human motion can be captured by a discrete vocabulary of learned motion tokens, and that anomalies are visible as failures to reconstruct through that vocabulary. The paper adapts a vector-quantized GAN to temporal keypoint sequences, training exclusively on normal motion and scoring each new sequence by reconstruction error. This yields in-domain accuracy of 81.83% AUC-ROC on HR-SHT, cross-domain accuracy of 76.69% on HR-SHT when trained only on the Panoptic dataset, and competitive cross-dataset robustness. The intended payoff is a pose-based anomaly detector that transfers across cameras and datasets without retraining, avoiding the appearance and privacy problems of pixel-based surveillance analysis.

What carries the argument

The load-bearing object is the vector-quantized autoencoder with adversarial training. A 1D temporal-convolutional encoder maps each keypoint sequence to a continuous latent, a finite codebook of 1024 learned motion embeddings replaces each latent by its nearest neighbor, and a decoder reconstructs the sequence from those discrete tokens. The training loss combines reconstruction MSE, a commitment loss that ties encoder outputs to codebook entries, and an adversarial loss from a discriminator that encourages temporally realistic motion. At inference, the squared reconstruction error over the quantized sequence acts as the anomaly score, and the codebook itself is the 'motion vocabulary' of normal behavior.

What would settle it

Take the same test videos, extract keypoints with two independent pose estimators, and run the frozen VQ-VAD model on both sets; if the AUC-ROC differs by more than a few points across estimators, or if adding controlled Gaussian noise to joint coordinates of normal sequences produces reconstruction errors as large as those of true anomalies, the anomaly signal is dominated by pose-estimation artifacts rather than behavioral content.

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Extended reading notes

Core claim

On its own terms, the paper establishes a new application of discrete representation learning to pose-based video anomaly detection. VQ-VAD encodes 48-frame keypoint windows into continuous latents, maps each latent to its nearest neighbor in a learned 1024-entry motion codebook, and reconstructs the keypoint sequence from the discrete tokens. Training on normal sequences only, the model uses mean-squared reconstruction error as the anomaly score, taking the maximum error over individuals in a frame. The central result is that this tokenized representation generalizes: training on the Panoptic lab dataset reaches 75.09% AUC-ROC on SHT and 76.69% on HR-SHT without any target-domain adaptation, and in-domain training reaches 81.83% on HR-SHT. The paper argues this is because the codebook learns transferable motion primitives rather than scene-specific appearance, and shows the largest gains over a normalizing-flow baseline on the harder HuVAD dataset under domain shift (59.66% vs 54.32%).

Load-bearing premise

The input keypoint sequences must be clean, consistently normalized, and directly comparable across the source and target datasets, even though the paper does not specify the pose estimator, keypoint normalization, or how 2D and 3D keypoints are aligned, so measured reconstruction errors could partly reflect pose-estimation noise rather than abnormal behavior.

Editorial extensions

If this is right

If the claim holds, pose-based anomaly detectors can be trained on one motion-capture domain and deployed on surveillance cameras without per-scene retraining, since the discrete tokens encode motion rather than appearance.
Reconstruction-error thresholding with standard criteria like equal error rate is sufficient for deployment, giving a practical anomaly alarm from a single score per frame.
Larger codebooks (up to 1024 tokens) and lower temporal compression (F4) improve reconstruction fidelity, so system designers can trade codebook size against compute for target accuracy.
Frame-level scoring as the maximum over individuals means one deviant person flags the frame, making the method conservative toward missed anomalies while raising the risk of false alarms in dense crowds.
The discrete representation supports interpretable analysis, since each reconstruction failure can be attributed to the nearest codebook entries the sequence fails to match.

Reading between the lines

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

A natural extension beyond the paper is to use the discrete token sequence itself, not just its reconstruction error, for anomaly localization: the specific codebook entries that fail to explain a motion window could indicate which body region deviates.
Because the paper does not specify the pose estimator or keypoint normalization, a controlled comparison of two pose detectors on identical video would reveal whether reconstruction errors reflect behavior or input noise; such an experiment would sharpen the method's claims of domain transfer.
The motion vocabulary might serve as a shared representation for other human-centric tasks, such as activity recognition or motion retrieval, since the same tokens are learned without anomaly labels.
An adaptive-scoring variant could maintain per-scene token statistics online and flag tokens that become rare, potentially improving sensitivity on datasets like HuVAD where the paper's cross-domain scores remain near 57-60%.

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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

Quick take: VQ-VAD is a sensible adaptation of VQ-GAN to temporal keypoints for anomaly detection, and the three-protocol evaluation is a real contribution. But the paper does not include the one experiment that would make its central claim stick: a matched continuous-latent autoencoder with the same encoder, decoder, and adversarial loss, minus the codebook. Without that, the observed AUCs on SHT/HR-SHT and the cross-domain numbers could come from the temporal conv architecture, the adversarial loss, or the reconstruction-error scoring. So the discrete vocabulary as the causal ingredient is unproven.

What's new: this is the first application of vector quantization to pose sequences for VAD as far as the cited literature goes. The framing of motion as a vocabulary is reasonable, and the cross-domain and cross-dataset protocols are useful additions. Reconstruction metrics on CMU Panoptic (Table 1) show expected trends with compression and codebook size, and the authors picked F4-K1024 based on those results, which is defensible.

Soft spots, in rough order:

1. Missing continuous-latent control. You need the same model with a continuous bottleneck. The stress-test flags this correctly. It is not fatal to the paper's usefulness, but it is fatal to the claim that discretization is what helps.

2. No error bars or significance tests. All AUCs are single runs, and the differences they emphasize (e.g., 59.66 vs 54.32 on HuVAD) are presented without variance, so we can't tell if they are real.

3. Pose preprocessing is a black box. The paper says keypoints are flattened, but never says which detector, how 2D/3D keypoints are aligned, or normalization. Since pose estimation noise can dominate reconstruction error, this matters.

4. Baseline comparisons are a bit uneven: in-domain VQ-VAD is behind STG-NF on the main benchmarks, and the advantage on HuVAD is used to claim robustness. That is fine, but the headline 'strong in-domain accuracy' overstates a middle-of-the-pack result.

Minor: hyperparameters chosen on validation is normal, not a flaw. The self-citations are mostly to the group's own prior work, but they are relevant and not excessive.

Bottom line: this is a solid submission for a workshop or a conference, provided the authors add the continuous-latent ablation and report error bars. The idea is worth publishing as an exploratory study. It deserves peer review, but it needs heavy revision before the central claim is believable.

Referee Report

4 major / 5 minor

Summary. The paper proposes VQ-VAD, a pose-based video anomaly detection framework that adapts VQ-GAN to temporal keypoint sequences. An encoder maps a keypoint sequence to a continuous latent, a learned codebook quantizes it into discrete motion tokens, and a decoder reconstructs the sequence; the reconstruction error serves as the frame-level anomaly score. The model is trained only on normal motion. Experiments cover three protocols: in-domain, cross-domain (CMU Panoptic to SHT/HR-SHT/NWPUC/HuVAD), and cross-dataset (SHT to NWPUC/HuVAD). The paper reports competitive AUC-ROC values, e.g., 81.83% in-domain on HR-SHT, 76.69% cross-domain on HR-SHT, and 59.66% cross-dataset on HuVAD.

Significance. If the claims hold, the paper introduces a plausible and novel direction for pose-based VAD: learning a discrete motion vocabulary that transfers across domains. The training and inference pipeline is coherent, the use of no anomaly labels is a strength, and the evaluation across three protocols is broader than typical in this area. The promise of public code is also a plus. However, the central attribution of the results to vector quantization is not supported by a matched continuous-latent control, and the single-run AUC values with no variance estimates weaken the quantitative claims. These issues are addressable but currently limit confidence in the paper's main contribution.

major comments (4)

[Section 4.2, Table 3] The central claim that discrete motion representations are beneficial is not isolated by any control condition. Every comparison in Table 3 is against a separately trained method with a different architecture, loss, and preprocessing. To support the claim that vector quantization is the decisive ingredient, the authors should compare VQ-VAD against a matched continuous-latent autoencoder using the same encoder, decoder, adversarial loss, and reconstruction-error scoring, with only the quantization step removed or replaced by a continuous bottleneck. Without this ablation, the reported in-domain and cross-domain results could be attributed to the temporal convolutional encoder-decoder, the adversarial loss, or the scoring mechanism rather than to the learned codebook.
[Section 3.1] The pose extraction and preprocessing pipeline is critically underspecified. The paper does not state which pose detector is used for SHT, HR-SHT, NWPUC, or HuVAD, how keypoints are normalized across datasets with different camera viewpoints and skeleton scales, or how 2D (CMU Panoptic) and 2D/3D keypoints from other benchmarks are aligned into the common representation X ∈ R^{F×C}. Since all anomaly scores are computed in this input space, pose-estimation noise or inconsistent coordinate conventions could dominate the reconstruction error and drive the reported AUC differences. The authors should specify the detector, normalization, and any keypoint-confidence filtering, and ideally include an analysis of pose-estimation noise on the anomaly scores.
[Section 4.2, Tables 3 and 4] All AUC-ROC results are reported as single runs with no standard deviations, confidence intervals, or significance tests. This is especially important for the headline cross-dataset and cross-domain comparisons: the HuVAD in-domain gap (VQ-VAD 65.33 vs. STG-NF 57.57) and the cross-dataset gap (59.66 vs. 54.32) could be within run-to-run variance, given the typical instability of adversarial training. The authors should report mean and standard deviation over multiple seeds (at least three) and, for the key comparisons, a paired significance test or an alternative such as multiple-run bootstrap intervals.
[Section 4.1.3 and 4.2] The model configuration F4-K1024 is selected solely on reconstruction metrics from the CMU Panoptic validation set (Table 1), but no ablation studies the effect of codebook size K or temporal compression factor on anomaly detection performance. The mapping from reconstruction quality (MPJPE, L1, TS) to anomaly detection AUC is not established. To strengthen the paper, the authors should report at least a small ablation varying K and compression in the in-domain setting on one benchmark (e.g., SHT), showing whether the configuration chosen for reconstruction is also optimal for anomaly discrimination.

minor comments (5)

[Section 3.1, Eq. (3)] The formula labeled L1 reconstruction error appears to square the absolute difference (the notation 'ห...หଶ' with subscript 2), which is inconsistent with the text describing an average of absolute differences and with the name L1. Please correct the formula to use the absolute value without squaring, or rename the metric.
[Table 1] The first row is labeled '𝐹4−𝐾2556' which appears to be a typo for F4-K256; also, the ordering of rows is not strictly by increasing codebook size or compression, which makes the table harder to read.
[Table 2] Table 2 lists SHT, NWPUC, and HuVAD but omits HR-SHT, although HR-SHT is a primary benchmark in Table 3. Please add the HR-SHT statistics or explain that they are part of the SHT row.
[References] References [24] and [25] appear unrelated to video anomaly detection (radiation therapy planning and influencer impact on affective polarization). Please verify whether these citations are necessary and, if not, remove them to avoid distracting the reader.
[Section 4.2.3 and Table 4] The cross-dataset experiment compares only VQ-VAD and STG-NF. Since the paper claims 'stronger robustness of VQ-VAD's discrete motion representations under domain shift,' including at least one additional recent pose-based baseline with published cross-dataset numbers would make the comparison more convincing.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: VQ-VAD's reconstruction-error scores come from an unlabeled normal-only-trained model and are reported as threshold-free AUC-ROC.

full rationale

The derivation chain is not circular. Section 3.2 defines the anomaly score as the reconstruction error ||X - X_hat||^2 produced by an encoder-quantizer-decoder trained end-to-end on normal sequences only, with no anomaly labels in the training objective. The reported in-domain, cross-domain, and cross-dataset numbers in Tables 3 and 4 are AUC-ROC, a threshold-free ranking metric, so no threshold is fit to test labels; the threshold discussion in Section 3.2 is explicitly for deployment and is not used in the reported scores. Hyperparameters such as temporal compression F4 and codebook size K=1024 are selected in Section 4.1.3 by reconstruction quality (MPJPE, L1) on the CMU Panoptic validation split, not by anomaly-detection test performance; this is standard model selection, not circularity. Self-citations ([1], [6], [7], [19], [20], [26]) appear only as related-work context, benchmark citations, or comparison baselines; no load-bearing premise is justified solely by a self-citation, and no uniqueness theorem is imported from the authors' prior work. The absence of a matched continuous-latent ablation is a genuine internal-validity concern for the claim that quantization, rather than the architecture or adversarial loss, drives performance, but that is a missing control, not an equivalence between prediction and input. No equation or definition reduces the reported results to the training objective or to a fitted parameter by construction. Therefore no circular step is exhibited.

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

The central claim rests on standard VAD modeling assumptions and a small set of chosen hyperparameters. No new physical or conceptual entities are introduced; the motion codebook is a learned representation, not a postulated extra entity.

free parameters (3)

Codebook size K = 1024
Selected from reconstruction validation in Table 1; larger codebooks were not tested, and this value is fixed for all anomaly detection experiments.
Temporal compression factor = 4x
F4 gives the best MPJPE and L1 reconstruction on the CMU Panoptic validation set and is then used for all anomaly detection runs.
Adversarial loss weight lambda = 0.5
Chosen by hand in the training objective in Section 3.2; no sensitivity analysis is reported.

assumptions (3)

domain assumption Reconstruction error separates normal from abnormal motion: normal motions are well covered by the learned codebook and anomalies are not.
This is the core detection premise in Section 3.2 ('Final anomaly decision'). It is assumed rather than derived and is standard for reconstruction-based VAD, but is not validated per dataset.
domain assumption Keypoint inputs are accurate enough that reconstruction error reflects behavioral abnormality rather than pose-estimation noise.
No pose detector or normalization is specified in Section 3.1, so this premise is unchecked and could affect all reported numbers.
domain assumption A motion codebook trained on CMU Panoptic transfers to real surveillance scenes.
Used for cross-domain evaluation in Section 4.2.1. It is empirically tested for one source dataset but is not otherwise justified.

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Cite this review

Pith. "Pith review of VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection." pith.science (2026). https://pith.science/paper/5UVWUVQM

@misc{pith2026260805069,
  author       = {Pith},
  title        = {Pith review of: VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5UVWUVQM}},
  note         = {Machine review of arXiv:2608.05069}
}

read the original abstract

Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance. To mitigate visual noise and address privacy concerns, recent work has shifted to pose-based VAD, which focuses on motion dynamics rather than raw video data. However, existing pose-based approaches model human behavior in continuous latent spaces, limiting their ability to learn compact motion patterns necessary for robust behavior analysis. We address this by proposing Vector-Quantized Video Anomaly Detection (VQ-VAD), a novel human-centric anomaly detection framework that learns discrete motion representations. VQ-VAD adapts Vector-Quantized GAN (VQ-GAN), originally developed for image generation, to operate on keypoint sequences and construct a motion codebook of normal behavior. Trained exclusively on normal motion sequences, VQ-VAD detects anomalies by identifying high reconstruction errors when an observed motion sequence cannot be mapped to the learned codebook. We conduct extensive experiments across three complementary evaluation settings, including in-domain, cross-domain, and cross-dataset generalization, on four anomaly detection benchmarks. VQ-VAD achieves strong in-domain accuracy (81.83% on HR-SHT [15]), effective cross-domain transfer from CMU Panoptic [14] (76.69% on HR-SHT [15] without retraining), and competitive cross-dataset robustness. The code base for this work is available at https://github.com/TeCSAR-UNCC/VQ-VAD.

Figures

Figures reproduced from arXiv: 2608.05069 by the authors.

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Reference graph

Works this paper leans on

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Pith tools

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

[1] [1]

Understanding the challenges and opportunities of pose-based anomaly detection

Ghazal Alinezhad Noghre, Armin Danesh Pazho, Vinit Katariya, and Hamed Tabkhi. Understanding the challenges and opportunities of pose-based anomaly detection. In Proceedings of the 8th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence, New York, NY, USA, 2023. Association for Computing Machinery

work page 2023

[2] [2]

Multi-task learning based video anomaly detection with attention

Mohammad Baradaran and Robert Bergevin. Multi-task learning based video anomaly detection with attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2886–2896, 2023

work page 2023

[3] [3]

A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation

Congqi Cao, Yue Lu, Peng Wang, and Yanning Zhang. A new comprehensive benchmark for semi-supervised video anomaly detection and anticipation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20392–20401, 2023

work page 2023

[4] [4]

Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection

Xiaoyu Chen, Shichao Kan, Fanghui Zhang, Yigang Cen, Linna Zhang, and Damin Zhang. Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection. Journal of Visual Communication and Image Representation, 90:103707, 2023

work page 2023

[5] [5]

Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation

Kai-Wen Cheng, Yie-Tarng Chen, and Wen-Hsien Fang. Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Transactions on Image Processing, 24(12):5288–5301, 2015

work page 2015

[6] [6]

Chad: Charlotte anomaly dataset

Armin Danesh Pazho, Ghazal Alinezhad Noghre, Babak Rahimi Ardabili, Christopher Neff, and Hamed Tabkhi. Chad: Charlotte anomaly dataset. In Scandinavian Conference on Image Analysis, pages 50–66. Springer, 2023

work page 2023

[7] [7]

Towards adaptive human-centric video anomaly detection: A comprehensive framework and a new benchmark

Armin Danesh Pazho, Shanle Yao, Ghazal Alinezhad Noghre, Babak Rahimi Ardabili, Vinit Katariya, and Hamed Tabkhi. Towards adaptive human-centric video anomaly detection: A comprehensive framework and a new benchmark. arXiv e-prints, pages arXiv–2408, 2024

work page 2024

[8] [8]

Taming transformers for high-resolution image synthesis

Patrick Esser, Robin Rombach, and Bj¨orn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12873–12883. IEEE, 2021

work page 2021