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REVIEW 4 major objections 5 minor 54 references

Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper claims that a parallel multi-scale framework that predicts velocity and acceleration increments between frames improves human motion forecasting accuracy by 16.3%-64.2% over previous methods, with gains on four benchmark…

desk verdict The multi-scale incremental idea is sensible, but the reported MPJPE numbers appear to be computed in normalized coordinates, so the SOTA claims do not hold as written. read the letter →

arxiv 2412.11632 v1 pith:DMRDI74G submitted 2024-12-16 cs.RO cs.AI

Juncheng Zou This is my paper · ORCID
classification cs.ROcs.AI
keywords humanmotionpredictionincrementalmodelingmulti-scalearchitecturemulti-stagetraininghuman-robotcollaborationvelocityincrementsaccelerationMPJPE
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 sets out to improve long-term human motion prediction for human-robot collaboration. Its central claim is that explicitly modeling the velocity and acceleration differences between consecutive frames, at several time scales at once, gives a model better short- and long-term forecasts than predicting absolute joint positions. The proposed PMS framework computes these increments in parallel branches, refines them iteratively, and trains with a loss that spans past, present, and future frames. The authors report state-of-the-art results on Human3.6M, CMU Mocap, 3DPW, and AMASS-BMLrub, with prediction accuracy improved by 16.3%-64.2% over previous methods. A sympathetic reader would take the core claim to be that incremental multi-scale dynamics is a more learnable target for motion forecasting than raw poses.

What carries the argument

The load-bearing object is the multi-scale incremental computation module: it converts normalized joint-position history into velocity and acceleration differences at intervals of 10, 5, and 2 frames, then synthesizes them with weighted sums (Eqs. 6-7). Parallel LSTM branches learn future increments from these features, and the acceleration branch corrects the velocity branch (Eq. 16) before attenuation regulation produces the pose (Eq. 17). An iterative adjustment mechanism and a full-timeline loss $L_t = L_p + L_c + L_f$ tie the multi-scale increments to both short- and long-range temporal context. This machinery is what carries the argument: it replaces absolute-pose regression with a hierarchy of motion increments.

What would settle it

Re-run the evaluation by inverting the per-action normalization (Eqs. 1-2) on the predicted poses before computing MPJPE, then compare the denormalized errors with the same baselines on Human3.6M and 3DPW; if the errors jump from the reported 0.1-1 range to baseline-scale values, the state-of-the-art claim would not survive.

Watch

Extended reading notes

Core claim

The paper's central claim is that PMS, a Parallel Multi-scale Incremental Prediction framework, can forecast human poses more accurately by predicting how joint positions change rather than predicting absolute positions. Given 50 observed frames, it computes velocity differences $\Delta v_k$ and acceleration differences $\Delta\Delta v_k$ over three time intervals ($\delta = 10$, $5$, and $2$ frames), combines them with learned weights, and runs parallel LSTM branches that predict future increments. The predicted acceleration is used to correct the predicted velocity before attenuation-regulated integration reconstructs future poses. A full-timeline loss sums L1 errors over past, present, and future horizons. On Human3.6M, CMU Mocap, 3DPW, and AMASS-BMLrub, the authors report that PMS outperforms the compared baselines and raises prediction accuracy by 16.3%-64.2%.

Load-bearing premise

The claim rests on comparing errors computed after scaling each action's coordinates to [-1,1] against baseline errors, without an explicit step that scales the predictions back to original units before computing MPJPE.

Editorial extensions

If this is right

  • If the central claim is right, modeling inter-frame velocity and acceleration increments is a better inductive bias for human motion forecasting than predicting absolute joint positions, and it reduces error accumulation over longer horizons.
  • The multi-scale branches let the model capture both quick joint-level changes and whole-body trajectory shifts, which is exactly what a robot sharing workspace with a person needs for collision avoidance.
  • The full-timeline loss, by supervising past, present, and future frames jointly, should make predictions more continuous and less prone to the blurry mean-pose problem of recurrent predictors.
  • Because the gains are reported on four datasets with different capture protocols, the incremental representation should transfer across mocap systems and action vocabularies.

Reading between the lines

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

  • Beyond the paper, the same incremental velocity/acceleration representation could be applied to non-autoregressive transformers or diffusion-based motion generators, which currently predict absolute poses or residual positions rather than multi-scale increments.
  • Beyond the paper, the multi-scale increments could be extended to predict uncertainty: instead of point increments, a model could output a distribution over velocity/acceleration changes and sample consistent motion sequences.
  • Beyond the paper, the full-timeline loss recipe (past/current/future with multiple strides) is a transferable training strategy for any sequential predictor, not only human-motion models.
  • Beyond the paper, replacing the hand-set interval weights with an attention mechanism over scales might let the model adaptively emphasize short-range increments for fast actions and long-range increments for sustained motions.
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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 paper proposes Parallel Multi-scale Incremental Prediction (PMS), a framework for 3D human motion prediction that computes velocity and acceleration increments over multiple time intervals (10, 5, and 2 frames), fuses them through parallel LSTM branches, and trains with a multi-term full-timeline loss. The authors report experiments on Human3.6M, CMU Mocap, 3DPW, and AMASS-BMLrub, claiming state-of-the-art performance with 16.3%–64.2% accuracy improvements over prior methods, and they include an ablation study over network components and loss variants.

Significance. If the reported results were valid, the multi-scale incremental modeling idea would be a useful addition to human motion prediction, and the paper's attempt to model both velocity and acceleration increments across temporal scales is a reasonable inductive bias to investigate. The authors also provide a clear architectural description, evaluate on four datasets, and include a component-wise ablation in Table 6. However, the empirical foundation of the central claim is not sound as presented: the evaluation appears to compare normalized-coordinate errors against raw-coordinate baselines, several tables contain implausible or internally inconsistent numbers, and the headline improvement range is drawn from selected favorable action/horizon pairs. The paper does not supply code, data, or enough hyperparameter detail (e.g., the weighting coefficients in Eqs. 6–7 and attenuation coefficients in Eq. 17) to reproduce the experiments independently.

major comments (4)
  1. [§3.2, Eqs. (1)–(2) and §4.2, Eq. (22)] The paper normalizes each action's joint coordinates to [-1,1] using Eqs. (1)-(2) and trains on those normalized coordinates, but nowhere states that predictions are denormalized before applying the MPJPE formula in Eq. (22). As written, the reported errors are dimensionless fractions of the per-action coordinate range, not errors in millimeters, and they cannot be compared to baseline errors computed on raw coordinates. Table 4's 3DPW values (0.09 at 80 ms, 0.18 at 1000 ms) are physically implausible in millimeters but natural in a [-1,1] space. Because the entire state-of-the-art claim rests on these comparisons, this unit mismatch must be resolved; if the predictions are not denormalized, all tables must be recomputed.
  2. [Abstract and Table 2] The claimed improvement range of 16.3%–64.2% is cherry-picked from selected favorable action/horizon pairs (notably Sitting Down, Posing, and short-term Sitting Down/Taking Photo) rather than from the full set of results. Table 2 shows that PMS is substantially worse than Rele-GCN on Walking at 560 ms (62.5 vs. 31.9) and at 1000 ms (158.4 vs. 37.6), and worse than Rele-GCN on Eating at 560 ms (33.0 vs. 27.4). The abstract's global statement is therefore not supported by the complete table; the authors should either report aggregate results across all actions and horizons or explicitly restrict the claim to the actions for which it holds.
  3. [Table 5 and §4.3 (AMASS-BMLrub results)] The AMASS-BMLrub results are internally inconsistent. PMS is reported as 8.01 at 320 ms, then 2.26 at 400 ms and 2.31 at 560 ms, a non-monotonic error pattern that is implausible for cumulative forecast error on the same test set. The accompanying text states that PMS reduces errors by 79.2%, 63.5%, 55.5%, 89.7%, 92.0%, 6.7%, and 15.3% across seven indicators, but the table implies reductions of 76.7% at 1000 ms and 78.4% on average, not 6.7% and 15.3%. These discrepancies indicate that the reported numbers are not reliable as absolute errors and that the evaluation pipeline needs to be corrected and re-run.
  4. [§4.3, Table 2 discussion] The narrative that PMS 'outperforms other methods for 6 actions' in the long-term Human3.6M comparison is contradicted by the table: at 560 ms, PMS is worse than the best baseline on Walking, Eating, and Smoking, and is better than the best baseline on 12 of the 15 listed actions. The statement that PMS achieves better performance on '20 out of 32 metrics' is not defined in a verifiable way (it is unclear whether other methods are compared per action and horizon separately, or per action averaged over horizons). Please report exact counts and specify the comparison protocol.
minor comments (5)
  1. [§4.2, Eq. (22)] The notation in Eq. (22) uses p and p' for ground-truth and predicted positions, but the problem definition in §3.1 uses p_t and q_t; the two notations should be unified for clarity.
  2. [References] References [45] and [49] are the same paper (Mao et al., 'Learning trajectory dependencies for human motion prediction', ICCV 2019), yet they are listed as two distinct references. This should be corrected.
  3. [Tables 1 and 2] The Rele-GCN row is missing for the WalkingTogether action in both Table 1 and Table 2, although the text repeatedly compares PMS against Rele-GCN. Please either provide the missing values or explicitly state that Rele-GCN was not evaluated on that action.
  4. [§4.1, 3DPW setup] The description of the 3DPW dataset says each sample contains 72 positions, whereas Human3.6M uses 28 joints with 3 coordinates each; the correspondence between the 72 values and the joint definitions is not explained, and the per-action normalization described in Eqs. (1)-(2) is not specified for 3DPW.
  5. [§3.2, Eqs. (6)–(7) and (17)] The multi-scale weighting coefficients alpha_k, beta_k and the attenuation coefficients gamma_i are introduced with summation constraints but no values, initialization scheme, or learning procedure. The ablation entry labeled '0.4 0.6' is the only concrete numerical hint, and it is not tied directly to the equations, so the reported method is not fully reproducible from the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: PMS predictions are produced by a trained network and evaluated on held-out test data; the main concerns are metric comparability and reporting consistency, not circularity.

full rationale

The paper's central claim is an empirical accuracy improvement from the learned PMS model measured by MPJPE on four datasets. The derivation chain is: normalize coordinates (Eqs. 1-2), compute multi-scale velocity and acceleration increments (Eqs. 3-5), combine them with weighting coefficients (Eqs. 6-7), process them through learned LSTM and fully connected layers (Eqs. 8-15), correct the velocity prediction by the acceleration prediction (Eq. 16), apply an attenuation mechanism (Eq. 17), and train with a full-timeline loss (Eqs. 18-21). The reported predictions are outputs of the trained network on test sequences; they are not defined as the training inputs, the fitted weights, or the manually chosen coefficients. The state-of-the-art comparison is therefore a benchmark result rather than a tautology. No load-bearing step is justified by a self-citation, no uniqueness theorem from the authors' prior work is invoked, and no established result is merely renamed by the new framework. The most serious issue in the paper is not circularity but empirical validity: Section 3.2 normalizes joint coordinates to [-1,1], and Section 4.2 defines MPJPE in Eq. 22 without an explicit denormalization step, so the reported errors on 3DPW and AMASS may not be in the same units as the baseline errors; the AMASS results also show non-monotonic errors across horizons. These are substantial evaluation risks, but they do not make the model's predictions equivalent to its inputs by construction. Therefore the circularity score is 0.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim depends on several unspecified coefficients and on the unstated assumption that normalized-coordinate errors are comparable across methods.

free parameters (4)
  • Multi-scale velocity weighting coefficients alpha_k = unspecified
    Eq. (6) defines a weighted combination of velocity differences at intervals 10, 5, 2; the coefficients are not given in the paper, only an ablation variant '0.4 0.6' is mentioned (Section 4.4).
  • Multi-scale acceleration weighting coefficients beta_k = unspecified
    Eq. (7) similarly defines a weighted combination of acceleration differences; the coefficient values are not specified.
  • Attenuation coefficients gamma_i = unspecified
    Eq. (17) uses attenuation coefficients to adjust predicted poses from velocity increments; their values are never stated.
  • Time intervals {10,5,2} = 10, 5, 2 frames
    The multi-scale intervals are chosen by hand (Eq. 3) rather than learned or justified.
assumptions (3)
  • domain assumption Human motion can be represented by velocity and acceleration differences computed at fixed time intervals of 10, 5, and 2 frames.
    The entire incremental computation module (Section 3.2, Eqs. 3-7) relies on this representational assumption.
  • domain assumption Normalizing each action sequence to [-1,1] per action does not change the comparability of MPJPE values across methods and datasets.
    Section 3.2 applies per-action normalization, but Section 4.2 (Eq. 22) computes MPJPE without an explicit denormalization step, so this assumption is load-bearing for the reported results.
  • standard math LSTM and fully connected layers with batch normalization and dropout can learn the mapping from historical increments to future increments.
    The incremental learning module (Eqs. 8-15) assumes standard deep learning capacity; no proof is given.

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Pith. "Pith review of Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration." pith.science (2026). https://pith.science/paper/DMRDI74G

@misc{pith2026241211632,
  author       = {Pith},
  title        = {Pith review of: Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DMRDI74G}},
  note         = {Machine review of arXiv:2412.11632}
}
read the original abstract

Accurate human motion prediction is crucial for safe human-robot collaboration but remains challenging due to the complexity of modeling intricate and variable human movements. This paper presents Parallel Multi-scale Incremental Prediction (PMS), a novel framework that explicitly models incremental motion across multiple spatio-temporal scales to capture subtle joint evolutions and global trajectory shifts. PMS encodes these multi-scale increments using parallel sequence branches, enabling iterative refinement of predictions. A multi-stage training procedure with a full-timeline loss integrates temporal context. Extensive experiments on four datasets demonstrate substantial improvements in continuity, biomechanical consistency, and long-term forecast stability by modeling inter-frame increments. PMS achieves state-of-the-art performance, increasing prediction accuracy by 16.3%-64.2% over previous methods. The proposed multi-scale incremental approach provides a powerful technique for advancing human motion prediction capabilities critical for seamless human-robot interaction.

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Reference graph

Works this paper leans on

54 extracted references · 52 canonical work pages

  1. [1]

    Spatio-Temporal Branching for Motion Prediction using Motion Increments

    Wang J, Zhou Y, Qiang W, et al. Spatio-Temporal Branching for Motion Prediction using MotionIncrements[J].arXivpreprintarXiv:2308.01097,2023

    work page Pith review arXiv 2023

  2. [2]

    Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

    Zand M, Etemad A, Greenspan M. Multiscale Residual Learning of Graph Convolutional SequenceChunksforHumanMotionPrediction[J].arXivpreprintarXiv:2308.16801,2023

    work page Pith review arXiv 2023

  3. [3]

    Future video frame prediction based on generative motion-assistant discriminativenetwork[J].AppliedSoftComputing,2023,135:110028

    Li C, Chen X. Future video frame prediction based on generative motion-assistant discriminativenetwork[J].AppliedSoftComputing,2023,135:110028

    work page 2023

  4. [4]

    Pose transformers (potr): Human motion prediction with non-autoregressive transformers[C]//Proceedings of the IEEE/CVF InternationalConferenceonComputerVision.2021:2276-2284

    Martínez-González A, Villamizar M, Odobez J M. Pose transformers (potr): Human motion prediction with non-autoregressive transformers[C]//Proceedings of the IEEE/CVF InternationalConferenceonComputerVision.2021:2276-2284

    work page 2021

  5. [5]

    Efficient convolutional hierarchical autoencoder for human motionprediction[J].TheVisualComputer,2019,35:1143-1156

    Li Y, Wang Z, Yang X, et al. Efficient convolutional hierarchical autoencoder for human motionprediction[J].TheVisualComputer,2019,35:1143-1156

    work page 2019

  6. [6]

    Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction[J]

    Liu X,Yin J, Liu J, et al. Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction[J]. IEEE Transactions on Circuits and Systems for Video Technology,2020,31(6):2133-2146. 22 / 25

    work page 2020

  7. [7]

    Temporal consistency two-stream CNN for human motion prediction[J].Neurocomputing,2022,468:245-256

    Tang J, Zhang J, Yin J. Temporal consistency two-stream CNN for human motion prediction[J].Neurocomputing,2022,468:245-256

    work page 2022

  8. [8]

    Collaborative Multi-dynamic Pattern Modeling for Human Motion Prediction[J]

    Tang J, Zhang J, Ding R, et al. Collaborative Multi-dynamic Pattern Modeling for Human Motion Prediction[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023

    work page 2023

Show all 54 references
  1. [9]

    Multi-Graph Convolution Network for Pose Forecasting[J]

    Ren H, Shi Y, Liang K. Multi-Graph Convolution Network for Pose Forecasting[J]. arXiv preprintarXiv:2304.04956,2023

    2023 arXiv

  2. [10]

    Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction[J]

    Li M, Chen S, Chen X, et al. Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2021,44(6):3316-3333

    2021

  3. [11]

    Class-guided human motion prediction via multi-spatial-temporal supervision[J].NeuralComputingandApplications,2023,35(13):9463-9479

    Li J, Pan H, Wu L, et al. Class-guided human motion prediction via multi-spatial-temporal supervision[J].NeuralComputingandApplications,2023,35(13):9463-9479

    2023

  4. [12]

    Skeleton-parted graph scattering networks for 3d human motion prediction[C]//European Conference on Computer Vision

    Li M, Chen S, Zhang Z, et al. Skeleton-parted graph scattering networks for 3d human motion prediction[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland,2022:18-36

    2022

  5. [13]

    Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction[C]//Proceedings of the IEEE/CVF International Conference on ComputerVision.2021:11467-11476

    Dang L, NieY, Long C, et al. Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction[C]//Proceedings of the IEEE/CVF International Conference on ComputerVision.2021:11467-11476

    2021

  6. [14]

    LiM,ChenS,ZhaoY,etal.Dynamicmultiscalegraphneuralnetworksfor3dskeletonbased human motion prediction[C]//Proceedings of the IEEE/CVF conference on computer vision andpatternrecognition.2020:214-223

    2020

  7. [15]

    Learning multiscale correlations for human motion prediction[C]//2021 IEEE International Conference on Development and Learning (ICDL)

    Zhou H, Guo C, Zhang H, et al. Learning multiscale correlations for human motion prediction[C]//2021 IEEE International Conference on Development and Learning (ICDL). IEEE,2021:1-7

    2021

  8. [17]

    Posegpt: Quantization-based 3d human motion generation and forecasting[C]//European Conference on Computer Vision

    Lucas T, Baradel F, Weinzaepfel P, et al. Posegpt: Quantization-based 3d human motion generation and forecasting[C]//European Conference on Computer Vision. Cham: Springer NatureSwitzerland,2022:417-435

    2022

  9. [18]

    Cai Y, Huang L, Wang Y, et al. Learning progressive joint propagation for human motion prediction[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16. Springer International Publishing, 2020: 226-242

    2020

  10. [19]

    Hua Y, Xuanzhe F, Yaqing H, et al. Towards efficient 3d human motion prediction using deformable transformer-based adversarial network[C]//2022 International Conference on RoboticsandAutomation(ICRA).IEEE,2022:861-867

    2022

  11. [20]

    PVRED:Aposition-velocity recurrent encoder-decoder for humanmotionprediction[J].IEEETransactionsonImageProcessing,2021,30:6096-6106

    WangH, Dong J, Cheng B, et al. PVRED:Aposition-velocity recurrent encoder-decoder for humanmotionprediction[J].IEEETransactionsonImageProcessing,2021,30:6096-6106

    2021

  12. [21]

    Skip-attention encoder–decoder framework for human motion prediction[J].MultimediaSystems,2022:1-10

    Zhang R, Shu X,Yan R, et al. Skip-attention encoder–decoder framework for human motion prediction[J].MultimediaSystems,2022:1-10

    2022

  13. [22]

    Weakly-supervised action transition learning for stochastic humanmotion prediction[C]//Proceedings of theIEEE/CVF Conferenceon ComputerVision andPatternRecognition.2022:8151-8160

    Mao W, Liu M, Salzmann M. Weakly-supervised action transition learning for stochastic humanmotion prediction[C]//Proceedings of theIEEE/CVF Conferenceon ComputerVision andPatternRecognition.2022:8151-8160. 23 / 25

    2022

  14. [23]

    Varnet: Exploring variations for unsupervised video prediction[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2018:5801-5806

    Jin B, Hu Y, Zeng Y, et al. Varnet: Exploring variations for unsupervised video prediction[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2018:5801-5806

    2018

  15. [24]

    GAN-Poser: an improvised bidirectional GAN model for human motion prediction[J]

    Jain D K, Zareapoor M, Jain R, et al. GAN-Poser: an improvised bidirectional GAN model for human motion prediction[J]. Neural Computing and Applications, 2020, 32(18): 14579-14591

    2020

  16. [25]

    Development of human motion prediction strategy using inceptionresidualblock[J].MultimediaToolsandApplications,2023:1-15

    Gupta S, Yadav G K, Nandi G C. Development of human motion prediction strategy using inceptionresidualblock[J].MultimediaToolsandApplications,2023:1-15

    2023

  17. [26]

    Learning dynamic relationships for 3d human motion prediction[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.2020:6519-6527

    Cui Q, Sun H, Yang F. Learning dynamic relationships for 3d human motion prediction[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.2020:6519-6527

    2020

  18. [27]

    Physics-based human motion estimation and synthesis from videos[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision

    Xie K, Wang T, Iqbal U, et al. Physics-based human motion estimation and synthesis from videos[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021:11532-11541

    2021

  19. [28]

    Motionaug: Augmentation with physical correction for human motion prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:6427-6436

    Maeda T, Ukita N. Motionaug: Augmentation with physical correction for human motion prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:6427-6436

    2022

  20. [29]

    PIMNet: Physics-Infused Neural Network for Human Motion Prediction[J].IEEERoboticsandAutomationLetters,2022,7(4):8949-8955

    Zhang Z, Zhu Y, Rai R, et al. PIMNet: Physics-Infused Neural Network for Human Motion Prediction[J].IEEERoboticsandAutomationLetters,2022,7(4):8949-8955

    2022

  21. [30]

    Physdiff: Physics-guided human motion diffusion model[C]//ProceedingsoftheIEEE/CVFInternationalConferenceonComputerVision.2023: 16010-16021

    Yuan Y, Song J, Iqbal U, et al. Physdiff: Physics-guided human motion diffusion model[C]//ProceedingsoftheIEEE/CVFInternationalConferenceonComputerVision.2023: 16010-16021

    2023

  22. [31]

    Long-term human motion prediction with scene context[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16

    Cao Z, Gao H, Mangalam K, et al. Long-term human motion prediction with scene context[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. Springer International Publishing, 2020: 387-404

    2020

  23. [32]

    Mahdavian M, Nikdel P, TaherAhmadi M, et al. STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Follow-Ahead[C]//2023IEEEInternationalConferenceonRoboticsandAutomation(ICRA). IEEE,2023:9959-9965

    2023

  24. [33]

    MotionRNN: A flexible model for video prediction with spacetime-varyingmotions[C]//ProceedingsoftheIEEE/CVF conferenceoncomputervision andpatternrecognition.2021:15435-15444

    Wu H, Yao Z, Wang J, et al. MotionRNN: A flexible model for video prediction with spacetime-varyingmotions[C]//ProceedingsoftheIEEE/CVF conferenceoncomputervision andpatternrecognition.2021:15435-15444

    2021

  25. [34]

    Long term motion prediction using keyposes[C]//2022InternationalConferenceon3DVision(3DV).IEEE,2022:12-21

    Kiciroglu S, Wang W, Salzmann M, et al. Long term motion prediction using keyposes[C]//2022InternationalConferenceon3DVision(3DV).IEEE,2022:12-21

    2022

  26. [35]

    Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction[J]

    Fu J, Yang F, Dang Y, et al. Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction[J]. IEEE Transactions on Neural Networks and Learning Systems,2023

    2023

  27. [36]

    Geometric algebra-based multiview interaction networks for 3D human motionprediction[J].PatternRecognition,2023,138:109427

    Zhong J, Cao W. Geometric algebra-based multiview interaction networks for 3D human motionprediction[J].PatternRecognition,2023,138:109427

    2023

  28. [37]

    Motionmixer: Mlp-based 3d human body pose forecasting[J].arXivpreprintarXiv:2207.00499,2022

    Bouazizi A, Holzbock A, Kressel U, et al. Motionmixer: Mlp-based 3d human body pose forecasting[J].arXivpreprintarXiv:2207.00499,2022

    2022 arXiv

  29. [38]

    Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction[J]

    Fu J, Yang F, Dang Y, et al. Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction[J]. IEEE Transactions on Neural Networks and Learning 24 / 25 Systems,2023

    2023

  30. [39]

    3d skeleton-based human motion prediction with manifold-aware gan[J]

    Chopin B, Otberdout N, Daoudi M, et al. 3d skeleton-based human motion prediction with manifold-aware gan[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2022

    2022

  31. [40]

    MaT,NieY,LongC,etal.Progressivelygeneratingbetterinitialguessestowardsnextstages for high-quality human motion prediction[C]//Proceedings of the IEEE/CVF Conference on ComputerVisionandPatternRecognition.2022:6437-6446

    2022

  32. [41]

    DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and PatternRecognition.2023:5527-5536

    Sun X, Sun H, Li B, et al. DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and PatternRecognition.2023:5527-5536

    2023

  33. [42]

    Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction[J]

    Li Q, WangY. Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction[J]. IEEE Transactions onEmergingTopicsinComputationalIntelligence,2023

    2023

  34. [43]

    Back to mlp: A simple baseline for human motion prediction[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of ComputerVision.2023:4809-4819

    Guo W, Du Y, Shen X, et al. Back to mlp: A simple baseline for human motion prediction[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of ComputerVision.2023:4809-4819

    2023

  35. [44]

    Bidirectional transformer gan for long-term human motion prediction[J]

    Zhao M, Tang H, Xie P, et al. Bidirectional transformer gan for long-term human motion prediction[J]. ACM Transactions on Multimedia Computing, Communications and Applications,2023,19(5):1-19

    2023

  36. [46]

    Space-time-separable graph convolutional network for pose forecasting[C]//Proceedings of the IEEE/CVF International Conference on ComputerVision.2021:11209-11218

    Sofianos T, Sampieri A, Franco L, et al. Space-time-separable graph convolutional network for pose forecasting[C]//Proceedings of the IEEE/CVF International Conference on ComputerVision.2021:11209-11218

    2021

  37. [47]

    Multi-level motion attention for human motion prediction[J].Internationaljournalofcomputervision,2021,129(9):2513-2535

    Mao W, Liu M, Salzmann M, et al. Multi-level motion attention for human motion prediction[J].Internationaljournalofcomputervision,2021,129(9):2513-2535

    2021

  38. [48]

    Ionescu C, Papava D, Olaru V, et al. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments[J]. IEEE transactions on pattern analysisandmachineintelligence,2013,36(7):1325-1339

    2013

  39. [49]

    Learning trajectory dependencies for human motion prediction[C]//Proceedings of the IEEE/CVF international conference on computer vision

    Mao W, Liu M, Salzmann M, et al. Learning trajectory dependencies for human motion prediction[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019:9489-9497

    2019

  40. [50]

    On human motion prediction using recurrent neural networks[C]//ProceedingsoftheIEEEconferenceoncomputervisionandpatternrecognition

    Martinez J, Black M J, Romero J. On human motion prediction using recurrent neural networks[C]//ProceedingsoftheIEEEconferenceoncomputervisionandpatternrecognition. 2017:2891-2900

    2017

  41. [51]

    Convolutional sequence to sequence model for human dynamics[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2018:5226-5234

    Li C, Zhang Z, Lee W S, et al. Convolutional sequence to sequence model for human dynamics[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.2018:5226-5234

    2018

  42. [52]

    VonMarcardT,HenschelR,BlackMJ,etal.Recoveringaccurate3dhumanposeinthewild using imus and a moving camera[C]//Proceedings of the European conference on computer vision(ECCV).2018:601-617

    2018

  43. [53]

    AMASS: Archive of motion capture as surface shapes[C]//ProceedingsoftheIEEE/CVF internationalconferenceoncomputervision.2019: 25 / 25 5442-5451

    Mahmood N, Ghorbani N, Troje N F, et al. AMASS: Archive of motion capture as surface shapes[C]//ProceedingsoftheIEEE/CVF internationalconferenceoncomputervision.2019: 25 / 25 5442-5451

    2019

  44. [54]

    PaddlePaddle: An open-source deep learning platform from industrialpractice[J].FrontiersofDataandDomputing,2019,1(1):105-115

    Ma Y, Yu D, Wu T, et al. PaddlePaddle: An open-source deep learning platform from industrialpractice[J].FrontiersofDataandDomputing,2019,1(1):105-115

    2019

  45. [55]

    Adam: A method for stochastic optimization[J]

    Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980,2014

    2014 arXiv

  46. [56]

    Incorporating Physics Principles for Precise Human Motion Prediction[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of ComputerVision.2024:6164-6174

    Zhang Y, Kephart J O, Ji Q. Incorporating Physics Principles for Precise Human Motion Prediction[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of ComputerVision.2024:6164-6174

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.