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REVIEW 4 major objections 5 minor 3 cited by

IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read IE-PONet claims that combining C3D spatiotemporal features, OpenPose real-time keypoints, and Bayesian hyperparameter tuning yields high-accuracy 3D pose estimation, with $AP^{p50}$ of 90.5 on NTURGB+D and 91.0 on FineGYM at about 8 GFLOPS.

desk verdict The paper's reported benchmark superiority is internally inconsistent and the 3D pose pipeline is never actually defined; this is a desk reject, not a revision. read the letter →

arxiv 2411.12676 v1 pith:AWCSRBGD submitted 2024-11-19 cs.CV cs.LG

Fei Ren , Chao Ren , Tianyi Lyu This is my paper · ORCID
classification cs.CVcs.LG
keywords HumanPoseEstimationIoTSensorsMotionCaptureMulti-ViewDeepLearningAthleteActionC3DOpen
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 proposes IE-PONet, a three-module pipeline for 3D pose estimation and motion analysis of athletes. It joins C3D for spatiotemporal video features, OpenPose for real-time body-keypoint detection, and Bayesian optimization to tune the model's hyperparameters. The authors report that this combination reaches $AP^{p50}$ scores of 90.5 on NTURGB+D and 91.0 on FineGYM, with mAP of 74.3 and 74.0, beating eight listed baselines while keeping computational cost near 8 GFLOPS. A coach or athlete could in principle use such a system for immediate technical feedback and injury-risk assessment during training.

What carries the argument

The load-bearing mechanism is the IE-PONet pipeline itself, a fusion of three established components: C3D, whose 3D convolutions extract spatiotemporal volume features; OpenPose, which detects body keypoints through heatmaps and Part Affinity Fields; and Bayesian optimization, which treats accuracy as a black-box function and guides hyperparameter search with a Gaussian-process surrogate. Bilinear pooling inside C3D fuses a feature system and an attention system to capture high-order motion detail. Each module is intended to add a distinct capability: motion dynamics, keypoint geometry, and automated tuning.

What would settle it

Reproduce the reported $AP^{p50}$ of 90.5 on NTURGB+D using only the described C3D, OpenPose, and Bayesian optimization components with the stated settings; if the 3D pose accuracy cannot be obtained because OpenPose outputs only 2D keypoints and no lifting mechanism is provided, the central accuracy claim would not be supported by the described architecture.

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Extended reading notes

Core claim

The paper's central claim is that merging complementary feature types—3D convolutional motion features, 2D keypoint detections, and Bayesian-tuned hyperparameters—outperforms each component alone and the existing baselines on two public action datasets. Ablation results support the claim by showing that removing C3D, OpenPose, or Bayesian optimization each lowers $AP^{p50}$ by roughly one to two points. The full model reports $AP^{p50}$ of 90.5/91.0 and mAP of 74.3/74.0 on NTURGB+D/FineGYM, ahead of the eight comparison models including HRNet-32 and skeleton-based graph networks.

Load-bearing premise

The pipeline's reported accuracy rests on the assumption that OpenPose's detected 2D keypoints are actually lifted to 3D and combined with C3D's spatiotemporal features by a feature-fusion step that the paper describes only in general terms, without a concrete method or standalone validation.

Editorial extensions

If this is right

  • If the reported numbers hold, real-time athlete pose analysis is achievable at the stated roughly 8 GFLOPS, which is within reach of edge or mobile processors used at training venues.
  • The ablation gaps imply that both motion features and keypoint geometry contribute materially, so removing either loses about two $AP^{p50}$ points.
  • The model is claimed to generalize across two different sport-action datasets, suggesting the same pipeline could be applied to other sports without architectural change.
  • The comparison table positions IE-PONet ahead of both video-based models and skeleton-based graph networks, suggesting the fusion route is competitive with both families.

Reading between the lines

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

  • The paper never specifies how OpenPose's 2D keypoint heatmaps are lifted to 3D poses or how they are fused with C3D features; a direct test would be to reimplement just that stage and measure its independent contribution.
  • A natural testable extension is to replace the undefined 3D lifting with an explicit depth-estimation or triangulation module and compare the reported scores.
  • The experiments use pre-recorded datasets, so the IoT real-time transmission and edge-computing claims remain untested in live training conditions; a field trial with streaming video would be needed to validate that part.
  • The gains over HRNet-32 (mAP 74.3 vs 73.4) may be largely attributable to Bayesian hyperparameter selection rather than the architecture; ablating with and without tuning at matched hyperparameters would isolate the source.
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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

4 major / 5 minor

Summary. The manuscript proposes IE-PONet, an IoT-oriented pipeline that combines C3D spatiotemporal features, OpenPose keypoint detection, and Bayesian hyperparameter optimization for 3D pose estimation and action analysis of athletes. Experiments on NTURGB+D and FineGYM are reported, with AP^p50 values around 90.5-91.0 and mAP values around 74.0-74.3 claimed to exceed eight baselines, supported by an ablation table. The central claim is an empirical benchmark assertion, but the manuscript contains an internal inconsistency between the results text and Table 3, and the described architecture does not specify how 2D keypoints are lifted to 3D poses.

Significance. If the reported results were reproducible, the paper would offer a modest engineering contribution: a fusion of off-the-shelf C3D and OpenPose with Bayesian tuning that achieves competitive pose-estimation accuracy at low computational cost for sports analytics. The paper's strengths are the use of standard datasets, explicit metric definitions, and an ablation structure. However, no code or model weights are provided, the evaluation protocol is underspecified, and the central empirical claim is contradicted by internal numbers, so the significance cannot currently be assessed from the manuscript.

major comments (4)
  1. [Section 4.6 / Table 3] The reported IE-PONet results are internally inconsistent. The text in Section 4.6 states NTURGB+D mAP=73.0 and AR=78.5, and FineGYM mAP=72.8 and AR=78.4, while Table 3 reports mAP=74.3/74.0 and AR=79.3/79.1 for the same model. The FineGYM AP^p50 and AP^p75 values also differ (90.7/81.0 in the text versus 91.0/81.5 in the table). This is not a cosmetic discrepancy: relative to the strongest baseline, HRNet-32 (mAP 73.4/73.0), the table version of IE-PONet is about 1 mAP better, whereas the text version is worse. The headline superiority claim is therefore not reproducible from the manuscript's own evidence.
  2. [Section 3.3] The claimed 3D pose output is never defined. Equations (10)-(17) describe preprocessing, 2D convolution and pooling, feature fusion of multi-layer feature maps, keypoint heatmaps, and keypoint grouping, but there is no triangulation, depth regression, or any other 2D-to-3D lifting operation. Section 3.5 states that 'feature fusion techniques' convert 2D keypoint data into 3D pose information, but no equation or architectural detail specifies this conversion. Because the paper's central claim is 3D pose estimation, this undefined fusion step is load-bearing and cannot be validated or reproduced.
  3. [Section 4.3 / 4.6] The evaluation protocol is underspecified. The paper does not state the train/validation/test split, whether the NTURGB+D cross-subject or cross-view protocol is used, how FineGYM clips are selected for pose estimation, or whether the baselines in Table 3 are retrained under identical conditions. No error bars, standard deviations, or repeated-run statistics are provided, and no code or trained models are released. Consequently, the numerical comparisons in Table 3 cannot be independently checked.
  4. [Section 3.4] The Bayesian optimization module is described only through generic equations, without the concrete settings needed to support the claimed contribution. No search space, number of evaluations, acquisition function details, or optimized hyperparameter values are reported, and Eq. (24) contains an unspecified trade-off parameter lambda. The ablation in Table 4 attributes a performance gain to Bayesian optimization, but the mechanism cannot be verified without these details.
minor comments (5)
  1. [Section 4.5] The text reports training 'accuracy' reaching approximately 0.95 on NTURGB+D and 0.90 on FineGYM, but accuracy is not defined for pose estimation, and its relationship to the AP/mAP metrics used elsewhere is unclear.
  2. [Section 4.6 / Table 3] The prose introducing Table 3 mentions input size as a reported metric, but the table has no input size column; the table should either include this information or the text should be corrected.
  3. [Section 3.1] The text refers to 'Figure 11' when describing the overall structure, but only Figure 1 is defined; this cross-reference should be fixed.
  4. [Throughout] The manuscript contains numerous grammatical errors and typos (e.g., 'there face challenges' in Section 2.1, '3 d' in the introduction), and the reference list includes many citations that appear unrelated to the topics under discussion; a careful language edit and reference relevance check are needed.
  5. [Section 3.5] The IoT component is described only conceptually, with no experiments involving actual IoT sensors, data transmission, or edge-computing devices; the title and framing overstate what is validated in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: IE-PONet's claimed performance is an empirical benchmark assertion, and no equation reduces a predicted quantity to a fitted input.

full rationale

The paper contains no derivation chain that could be circular in the sense defined here. The abstract and Section 4.6 report experimental benchmark numbers (AP, mAP, AR, GFLOPS) as measured outcomes, not as quantities derived from fitted parameters. Equations (1)-(17) are standard definitions of preprocessing, convolution, pooling, feature fusion, heatmaps, and keypoint grouping; Equations (18)-(24) are the standard Gaussian-process/expected-improvement statements of Bayesian optimization, with x* defined as argmax f(x) in Eq. (23) and no fitted hyperparameter values reported. Thus there is no fitted input that is later renamed as a prediction, no quantity defined in terms of the target result, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation. The reference list contains several papers involving co-author Tianyi Lyu (e.g., refs. [5], [12], [20], [41]), but these are background citations in the related-work sections and are not load-bearing for the architecture, the benchmark claims, or any optimization step. The paper does have serious verification problems, including an internal inconsistency between Section 4.6's mAP/AR values and Table 3, and an unspecified 2D-to-3D lifting procedure in Section 3.3; however, those are correctness, reproducibility, and missing-support issues, not circularity. No equation or citation reduces the paper's central claim to its own inputs.

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

The central claim rests on an unverified architecture and undisclosed hyperparameters. The 3D fusion step is asserted, the evaluation protocol is ambiguous, and no artifacts are shipped. This ledger shows that the paper adds no independent evidence beyond its own prose.

free parameters (3)
  • Hyperparameters tuned by Bayesian optimization (learning rate schedule, architecture depths, kernel sizes, fusion…
    Section 3.4 says the method optimizes hyperparameters via Eq. 23, but the selected values are never reported. The central performance claim therefore depends on undisclosed fitted settings.
  • Early stopping patience = 10 epochs
    Set by hand in Section 4.3 without sensitivity analysis; it affects which model checkpoint is selected and thus the reported metrics.
  • Batch size = 32
    Chosen in Section 4.3 without justification or sensitivity analysis; the reported metrics depend on it.
assumptions (3)
  • domain assumption OpenPose 2D keypoint detections can be lifted to 3D by an unspecified feature fusion step.
    Section 3.3 states that OpenPose 'generates 3D pose information of the athlete through feature fusion' without specifying the lifting mechanism, the depth supervision, or the training data.
  • domain assumption The evaluation metrics AP^p50 and mAP, as used in Tables 3 and 4, measure the same construct for both action recognition and pose estimation.
    Section 4.4 defines AP, mAP, and AR with generic formulas but does not specify the detection target (keypoint, box, or action class) or the IoU/OKS protocol, making the comparison to prior methods ambiguous.
  • ad hoc to paper C3D spatiotemporal features and OpenPose keypoints are complementary and can be combined by simple feature fusion.
    No evidence, learned fusion module, or ablation of fusion choices is presented. Equations 6 and 15 define a generic Fuse operation, but the paper never explains what it computes.
invented entities (1)
  • IE-PONet (IoT-Enhanced Pose Optimization Network)
    purpose: The proposed integration of C3D, OpenPose, and Bayesian optimization for athlete pose analysis.
    The network is described only in flow diagrams and generic equations. No weights, checkpoints, code, or deployment is provided, so its existence as a working system is not independently verifiable.

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Cite this review

Pith. "Pith review of IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose." pith.science (2026). https://pith.science/paper/AWCSRBGD

@misc{pith2026241112676,
  author       = {Pith},
  title        = {Pith review of: IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AWCSRBGD}},
  note         = {Machine review of arXiv:2411.12676}
}
abstract

This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extraction, OpenPose for real-time keypoint detection, and Bayesian optimization for hyperparameter tuning. Experimental results on NTURGB+D and FineGYM datasets demonstrate superior performance, with AP\(^p50\) scores of 90.5 and 91.0, and mAP scores of 74.3 and 74.0, respectively. Ablation studies confirm the essential roles of each module in enhancing model accuracy. IE-PONet provides a robust tool for athletic performance analysis and optimization, offering precise technical insights for training and injury prevention. Future work will focus on further model optimization, multimodal data integration, and developing real-time feedback mechanisms to enhance practical applications.

Figures

Figures reproduced from arXiv: 2411.12676 by the authors.

Figure 1
Figure 1. Overall flow chart of IE-PONet Model Structure. 3.2. C3D Module In the IE-PONet model, the C3D (Convolutional 3D Network) module is responsible for capturing the video data features of athletes’ movements. Compared to traditional 2D convolutional neural networks (2D CNNs), C3D has a stronger capability to extract spatiotemporal features, as it can capture both spatial and temporal information from the video [83, 84]… view at source ↗
Figure 2
Figure 2. Flow chart of the C3D Module Structure. Feature Output: 𝑋𝑜𝑢𝑡 = Output(𝑋𝑠𝑝𝑎𝑡𝑖𝑎𝑙_𝑡𝑒𝑚𝑝𝑜𝑟𝑎𝑙) (8) where Output denotes the output layer operation, which converts the spatiotemporal feature map into the final feature output, 𝑋𝑜𝑢𝑡. In the structure of the C3D module, in addition to the basic convolutional and pooling layers, bilinear pooling is employed for feature fusion between the feature system and the attention system… view at source ↗
Figure 3
Figure 3. Structural Flow chart of the OpenPose Module. to a fixed size required by the model, facilitating subse￾quent convolution operations. This preprocessing step not only eliminates differences in image size but also improves the efficiency of convolution operations. The preprocessed image data is input into the backbone network. The backbone network is the core part of the OpenPose module and is responsible for extract… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Structural Flowchart of Bayesian Optimization. The core idea of Bayesian optimization is to view the hy￾perparameter optimization problem as a sequential decision￾making process. At each step, Bayesian optimization first uses a probabilistic model (usually a Gaussian p…
Figure 5
Figure 5. Figure 5: Integration Flow of IoT System with IE-PONet Mode IoT sensors (such as cameras, accelerometers, and gy￾roscopes) are deployed in training venues and on athletes, responsible for real-time collection of various motion data. Cameras primarily capture video of athletes’ m…
Figure 6
Figure 6. Figure 6: Sample Images from NTURGB+D Datasets [90] [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Sample Images from FineGYM Datasets [91]. The diverse multi-view and 3D data provided by the NTURGB+D dataset offer a variety of training samples for our pose estimation and motion analysis, helping to improve the model’s generalization capability in different scenario…
Figure 8
Figure 8. Figure 8: Training results of the IE-PONet model on the NTURGB+D and FineGYM datasets. likely due to the dataset containing more complex and di￾verse gymnastics actions, requiring the model to adapt to more variations during learning. The training results of the IE-PONet model o…
Figure 9
Figure 9. Figure 9: Visualization results of the IE-PONet model on the FineGYM dataset. of three-dimensional convolutional neural network to ef￾fectively capture the spatial and temporal features in the video sequence, providing the ability to perceive the change and continuity of movemen…

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.