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REVIEW 2 major objections 4 minor 50 references

GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read GGMotion treats the human body as a set of groups updated by parallel dynamics-kinematics rules and reports state-of-the-art short-term motion prediction accuracy on three benchmarks.

desk verdict A genuinely new equivariant architecture with modest but consistent gains, held back by an auxiliary loss formula that does not match its stated purpose. read the letter →

arxiv 2507.07515 v2 pith:PQJZ62SD submitted 2025-07-10 cs.CV

Shuaijin Wan This is my paper · ORCID
classification cs.CV
keywords humanmotionpredictionequivariantgraphneuralnetworksdynamicsandkinematicsspatio-temporalradialfieldsgroupskeletontopologyMPJPE
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

GGMotion claims that human motion prediction improves when the skeleton is treated not as one abstract graph but as a set of body groups, each updated by explicit dynamics and kinematics rules that respect Euclidean geometry. The network uses spatio-temporal radial fields to aggregate geometric edge features, and equivariant MLPs keep the representation equivariant under rotations, reflections, and translations. Joint positions are updated in parallel through a rigid-body dynamics step followed by kinematic integration, with inter-group and intra-group interaction modules capturing dependencies at different scales. On Human3.6M, CMU-Mocap, and 3DPW, the method reports the best short-term average MPJPE among compared baselines, for instance 9.0 mm at 80 ms and 45.1 mm at 320 ms on Human3.6M, while stay within a compact parameter budget. The significance would be a lightweight, physically grounded predictor that produces more plausible short-term motion.

What carries the argument

The spatio-temporal radial field is the central mechanism: it computes spatial edge weights from Euclidean distances and hop counts between joints, and temporal edge weights from each joint's distance to the body centroid, then combines them with learnable scaling factors to form a motion force. This force is processed by an equivariant MLP whose attention is computed from a covariance matrix rather than a softmax, preserving $\mathrm{O}(3)$ equivariance. The dynamics-kinematics propagation then updates each group in parallel: Eq. (8) refines acceleration from force, position difference $r_{ij}$, and velocity difference $v_{ij}$, and Eqs. (9)-(10) integrate velocity and position.

What would settle it

Inspect the released code at the paper's repository and run the published $L_{\text{aux}}$ formula on predicted sequences: if average bone length over the test set decreases monotonically with training while the loss drops, the kinematic-constraint interpretation fails; alternatively, compare predictions trained with $L_{\text{aux}}$ to ones trained without it and measure whether per-bone variance relative to ground-truth bone lengths actually shrinks.

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Extended reading notes

Core claim

The paper's central claim is that explicit group-wise dynamics-kinematics modeling, combined with a spatio-temporal radial field, yields more accurate and physically plausible human motion prediction than full-body graph networks or simple upper/lower body splits. The model is equivariant under the orthogonal group $\mathrm{O}(3)$ and updates features through a parallel dynamics step that refines joint accelerations from position difference, velocity difference, and force, followed by kinematic integration of velocity and position. The authors report that this design achieves state-of-the-art average MPJPE on the three benchmarks for short-term horizons, including 9.0 mm at 80 ms and 45.1 mm at 320 ms on Human3.6M, and outperforms the equivariant baseline EqMotion at 1000 ms by a clear margin, which they attribute to the temporal edges of the radial field.

Load-bearing premise

The load-bearing premise is that the auxiliary loss term $L_{\text{aux}}$ enforces human skeleton kinematic constraints by preserving joint lengths, but the formula printed for it only sums L1 norms of predicted bone vectors without referencing any target length, so as written it would shrink bones toward zero instead of constraining them.

Editorial extensions

If this is right

  • Short-term prediction error on Human3.6M drops to 9.0 mm at 80 ms and 45.1 mm at 320 ms, below all compared baselines.
  • The same network generalizes to CMU-Mocap and 3DPW, improving average MPJPE across short-term horizons on both datasets.
  • Grouping into six body parts beats treating the whole body as one group or splitting into upper and lower halves, supporting the grouping strategy over fixed topology splits.
  • The temporal radial field contributes most of the long-term gain: removing it or the centroid update raises errors at 1000 ms, and the model stays competitive with spatio-temporal baselines at that horizon.
  • The model reaches these results with a small parameter count, which the paper attributes to preserved equivariance.

Reading between the lines

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

  • The published auxiliary loss formula penalizes the L1 norm of each predicted bone vector with no reference length, so the loss as written would drive bone lengths toward zero; if the released implementation instead compares to reference skeleton lengths, the paper's description and the ablation's interpretation do not match.
  • Enforcing invariance under reflections (det = -1 rotations) could remove handedness information relevant to actions like turning; a testable variant would restrict equivariance to proper rotations $\mathrm{SO}(3)$ and compare accuracy.
  • The fixed six-group partition is tuned for the Human3.6M skeleton; applying it to skeletons with different joint counts may require re-selecting groups, and a data-driven group assignment could transfer better across benchmarks.
  • The radial field's temporal edge is defined relative to the body centroid; a richer temporal model that also connects joints across time steps might close the remaining gap to DCT/TCN baselines in long-term prediction.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. The paper proposes GGMotion, a lightweight group-graph network for 3D human motion prediction. It combines a spatio-temporal radial field, inter-group/intra-group interaction modules, equivariant MLPs, and a parallel dynamics-kinematics update, and claims state-of-the-art short-term MPJPE on Human3.6M, CMU-Mocap, and 3DPW with only 0.46M parameters.

Significance. If the reported numbers are accurate, GGMotion would be a competitive lightweight equivariant model for short-term motion prediction, and the grouping strategy is a reasonable inductive bias. The paper offers a public code release, evaluates on three standard benchmarks, and includes ablations for each module, which is commendable. However, the auxiliary loss is not what it is claimed to be, and the equivariance proof is too terse to verify, so the central claims currently require revision before the results can be fully trusted.

major comments (2)
  1. [Section III-E, 'Loss Function'] The auxiliary loss L_aux as written in the unnumbered equation in Section III-E is the mean L1 distance between predicted positions of adjacent joints; it contains no reference to ground-truth bone lengths or rest lengths. Minimizing this sum pulls predicted joints together, so it is a shrinkage/smoothing regularizer rather than the advertised 'joint length' constraint. The text explicitly states that the loss 'introduce[s] joint length as an auxiliary loss' and Table IX credits this loss with consistent improvements, creating a direct contradiction between the stated purpose and the objective. If the released code implements a different comparison against reference lengths, Eq. (14) must be corrected; if the code matches the formula, the reported gains come from an undocumented regularizer. Please clarify and update the description and the ablation discussion accordingly.
  2. [Section III-C, 'Equivariant MLP'] The claim that phi_eq is O(3)-equivariant is not demonstrated. The covariance matrix Sigma_n = Z_q^T Z_k is invariant under rotations only if the learned linear layers W_q, W_k are equivariant maps or act solely on the feature dimension, but the manuscript does not specify their structure or show how R acts on the concatenated variables. The sentence 'after subtracting the centroid ... we can easily conclude phi_eq exhibits equivariance' is too terse for a load-bearing property; please provide a step-by-step proof or state the exact constraints on W_q, W_k, W_v and the MLP. Without this, the claimed benefit of 'preserving geometric equivariance' is not established.
minor comments (4)
  1. [Section V, Table VI] In the ablation text following Table VI, the sentence 'the T. field achieved lower prediction errors than the T. field' appears to have a typo; the second reference should presumably be 'S. field'. Please correct it.
  2. [Section IV-D, Table I] The reported improvements over the closest baselines are often below 1 mm (e.g., 9.0 vs 9.3 mm at 80 ms and 45.1 vs 45.8 mm at 320 ms on Human3.6M) and no variance or significance information is given; please report mean and standard deviation over multiple runs or seeds so the reader can judge the margin.
  3. [Section III-D, Eq. (8)] The dynamics update in Eq. (8) is a learned MLP over [f_j, r_ij, v_ij]; calling it the 'dynamics-kinematics algorithm' may overstate its connection to the hand-derived Eq. (1). Suggest rewording to 'learned dynamics-style update' or clarifying that it is parametric.
  4. [Section I, contributions] The contribution bullet describing the 'equivariant MLP integrated with the self-attention mechanism' would benefit from explicitly contrasting the covariance-based weighting with standard softmax attention, since the current text uses 'attention weight computation' only loosely.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported gains are empirical, and the equivariance construction is self-contained.

full rationale

The paper's central claim is an empirical model trained and evaluated on standard benchmarks (Human3.6M, CMU-Mocap, 3DPW); no fitted constant is renamed as a prediction, and no load-bearing premise is justified by the author's own prior work. The equivariance property is a construction: the covariance Sigma = Z_q^T Z_k is invariant under R in O(3) because R^T R = I, and multiplying the invariant attention weight by Z_v gives an equivariant output after centroid subtraction, so the derivation is self-contained and does not depend on any imported uniqueness theorem. The dynamics-kinematics update in Eq. (8) is presented as a learned parameterization inspired by Eq. (1), not as a theorem derived from it, and the predicted future poses are supervised directly by L_pos against ground truth, so the performance numbers are not forced by construction. The only notable inconsistency is Section III-E: L_aux = mean ||yhat_i - yhat_j||_1 contains no reference bone length, so the text's claim that this 'supervises kinematic constraints' is not supported by the equation; this is a correctness or description issue rather than a circular step and does not affect the circularity score.

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

The model relies on standard neural-network parameters and hand-chosen structural hyperparameters. The physical 'dynamics-kinematics' update is a learned equivariant MLP, not a parameter-free physical model. The only formal new entity is the spatio-temporal radial field, which is an architectural construct rather than a postulated physical object.

free parameters (6)
  • spatial scaling factors beta_i^l = learned
    Eq. (2) adjusts the influence of spatial edge features per joint per layer; fitted by gradient descent.
  • temporal scaling factors gamma_i^l = learned
    Eq. (5) modulates centroid-relative temporal features; fitted by gradient descent.
  • equivariant MLP weights (W_q, W_k, W_v, MLP, linear layers) = learned
    All network parameters are learned on the training set; the paper makes no parameter-free prediction.
  • number of body groups S = 6
    Chosen by hand and validated by ablation in Table VIII, not fitted to data.
  • number of blocks L = 4
    Chosen by hand; Table VII shows best performance at L=4.
  • auxiliary loss weight = 1 (implicit equal weight)
    Total loss is L = L_pos + L_aux with no reported balancing coefficient.
assumptions (4)
  • domain assumption Human joints undergo rigid-body motion around parent joints with fixed bone lengths
    Used to motivate Eq. (1) and the dynamics-kinematics design in Section III-B; ignores soft-tissue deformation and joint noise.
  • domain assumption O(3) equivariance holds for the motion prediction task
    Section III-A assumes motion is equivariant to rotation, reflection, and translation; if real human motion is not equivariant, the model loses accuracy.
  • ad hoc to paper Skeleton can be partitioned into six independent groups with inter and intra-group interactions
    Grouping into head, spine, arms, and legs is a design choice validated only by ablation on one dataset.
  • domain assumption Supervised ground-truth poses in the benchmarks are error-free
    Training and evaluation rely on marker-based and IMU-based poses; label noise is not modeled.

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Cite this review

Pith. "Pith review of GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction." pith.science (2026). https://pith.science/paper/PQJZ62SD

@misc{pith2026250707515,
  author       = {Pith},
  title        = {Pith review of: GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PQJZ62SD}},
  note         = {Machine review of arXiv:2507.07515}
}
read the original abstract

Human motion is a continuous physical process in 3D space, governed by complex dynamic and kinematic constraints. Existing methods typically represent the human pose as an abstract graph structure, neglecting the intrinsic physical dependencies between joints, which increases learning difficulty and makes the model prone to generating unrealistic motions. In this paper, we propose GGMotion, a group graph dynamics-kinematics network that models human topology in groups to better leverage dynamics and kinematics priors. To preserve the geometric equivariance in 3D space, we propose a novel radial field for the graph network that captures more comprehensive spatio-temporal dependencies by aggregating joint features through spatial and temporal edges. Inter-group and intra-group interaction modules are employed to capture the dependencies of joints at different scales. Combined with equivariant multilayer perceptrons (MLP), joint position features are updated in each group through parallelized dynamics-kinematics propagation to improve physical plausibility. Meanwhile, we introduce an auxiliary loss to supervise motion priors during training. Extensive experiments on three standard benchmarks, including Human3.6M, CMU-Mocap, and 3DPW, demonstrate the effectiveness and superiority of our approach, achieving a significant performance margin in short-term motion prediction. The code is available at https://github.com/inkcat520/GGMotion.git.

Figures

Figures reproduced from arXiv: 2507.07515 by the authors.

Figure 1
Figure 1. Previous methods perform prediction by aggregating features from [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Dynamics and kinematics analysis of the arm group. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The proposed GGMotion architecture. We embed the physical features computed from historical poses into the network through linear layers, preserving [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Intuitive comparison on Human3.6M. (a) Average Performance gain at different timestamps. (b) Average Performance gains on different actions. (c) [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Visualization of predicted poses of different methods on Human3.6M. The first row presents the ground truth in gray, while the second row displays [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Reference graph

Works this paper leans on

50 extracted references · 47 canonical work pages

  1. [1]

    3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception,

    S. Chen, B. Liu, C. Feng, C. Vallespi-Gonzalez, and C. Wellington, “3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception,”IEEE Signal Processing Magazine, vol. 38, no. 1, pp. 68–86, 2020

    work page 2020

  2. [2]

    Learning lane graph representations for motion forecasting,

    M. Liang, B. Yang, R. Hu, Y . Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” inCom- puter Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. Springer, 2020, pp. 541– 556

    work page 2020

  3. [3]

    Teaching robots to predict human motion,

    L.-Y . Gui, K. Zhang, Y .-X. Wang, X. Liang, J. M. Moura, and M. Veloso, “Teaching robots to predict human motion,” in2018 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 562–567

    work page 2018

  4. [4]

    A survey of human action analysis in hri applications,

    Y . Ji, Y . Yang, F. Shen, H. T. Shen, and X. Li, “A survey of human action analysis in hri applications,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 7, pp. 2114–2128, 2019

    work page 2019

  5. [5]

    A deep learning framework for character motion synthesis and editing,

    D. Holden, J. Saito, and T. Komura, “A deep learning framework for character motion synthesis and editing,”ACM Transactions on Graphics (ToG), vol. 35, no. 4, pp. 1–11, 2016

    2016

  6. [6]

    Auto-conditioned recurrent networks for extended complex human motion synthesis,

    Y . Zhou, Z. Li, S. Xiao, C. He, Z. Huang, and H. Li, “Auto-conditioned recurrent networks for extended complex human motion synthesis,” in International Conference on Learning Representations, 2018

    work page 2018

  7. [7]

    Recurrent network models for human dynamics,

    K. Fragkiadaki, S. Levine, P. Felsen, and J. Malik, “Recurrent network models for human dynamics,” inProceedings of the IEEE International Conference on Computer Vision (ICCV), December 2015

    work page 2015

  8. [8]

    Imitation learning for human pose prediction,

    B. Wang, E. Adeli, H.-k. Chiu, D.-A. Huang, and J. C. Niebles, “Imitation learning for human pose prediction,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 7124–7133

    work page 2019

Show all 50 references
  1. [9]

    Learning human motion models for long-term predictions,

    P. Ghosh, J. Song, E. Aksan, and O. Hilliges, “Learning human motion models for long-term predictions,” in2017 International Conference on 3D Vision (3DV). IEEE, 2017, pp. 458–466

    2017

  2. [10]

    Pvred: A position-velocity recurrent encoder-decoder for human motion prediction,

    H. Wang, J. Dong, B. Cheng, and J. Feng, “Pvred: A position-velocity recurrent encoder-decoder for human motion prediction,”IEEE Trans- actions on Image Processing, vol. 30, pp. 6096–6106, 2021

    2021

  3. [11]

    Structural-rnn: Deep learning on spatio-temporal graphs,

    A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-rnn: Deep learning on spatio-temporal graphs,” inProceedings of the ieee conference on computer vision and pattern recognition, 2016, pp. 5308– 5317

    2016

  4. [12]

    On human motion prediction using recurrent neural networks,

    J. Martinez, M. J. Black, and J. Romero, “On human motion prediction using recurrent neural networks,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2891–2900

    2017

  5. [13]

    Structured prediction helps 3d human motion modelling,

    E. Aksan, M. Kaufmann, and O. Hilliges, “Structured prediction helps 3d human motion modelling,” inProceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV), October 2019

    2019

  6. [14]

    Long-term human motion prediction by modeling motion context and enhancing motion dynamic,

    Y . Tang, L. Ma, W. Liu, and W. Zheng, “Long-term human motion prediction by modeling motion context and enhancing motion dynamic,” arXiv preprint arXiv:1805.02513, 2018

    2018 arXiv

  7. [15]

    History repeats itself: Human motion prediction via motion attention,

    W. Mao, M. Liu, and M. Salzmann, “History repeats itself: Human motion prediction via motion attention,” inComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. Springer, 2020, pp. 474–489

    2020

  8. [16]

    Pose trans- formers (potr): Human motion prediction with non-autoregressive trans- formers,

    A. Mart ´ınez-Gonz´alez, M. Villamizar, and J.-M. Odobez, “Pose trans- formers (potr): Human motion prediction with non-autoregressive trans- formers,” inProceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 2276–2284

    2021

  9. [17]

    Multi-level motion attention for human motion prediction,

    W. Mao, M. Liu, M. Salzmann, and H. Li, “Multi-level motion attention for human motion prediction,”International journal of computer vision, vol. 129, no. 9, pp. 2513–2535, 2021

    2021

  10. [18]

    A spatio-temporal transformer for 3d human motion prediction,

    E. Aksan, M. Kaufmann, P. Cao, and O. Hilliges, “A spatio-temporal transformer for 3d human motion prediction,” in2021 International Conference on 3D Vision (3DV). IEEE, 2021, pp. 565–574

    2021

  11. [19]

    Humanmac: Masked motion completion for human motion prediction,

    L.-H. Chen, J. Zhang, Y . Li, Y . Pang, X. Xia, and T. Liu, “Humanmac: Masked motion completion for human motion prediction,” inProceed- ings of the IEEE/CVF international conference on computer vision, 2023, pp. 9544–9555

    2023

  12. [20]

    Auxiliary tasks benefit 3d skeleton-based human motion prediction,

    C. Xu, R. T. Tan, Y . Tan, S. Chen, X. Wang, and Y . Wang, “Auxiliary tasks benefit 3d skeleton-based human motion prediction,” inProceed- ings of the IEEE/CVF international conference on computer vision, 2023, pp. 9509–9520

    2023

  13. [21]

    Toward realistic 3d human motion prediction with a spatio- temporal cross-transformer approach,

    H. Yu, X. Fan, Y . Hou, W. Pei, H. Ge, X. Yang, D. Zhou, Q. Zhang, and M. Zhang, “Toward realistic 3d human motion prediction with a spatio- temporal cross-transformer approach,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 10, pp. 5707–5720, 2023

    2023

  14. [22]

    Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction,

    M. Li, S. Chen, Y . Zhao, Y . Zhang, Y . Wang, and Q. Tian, “Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 214–223

    2020

  15. [23]

    Spatio-temporal gating- adjacency gcn for human motion prediction,

    C. Zhong, L. Hu, Z. Zhang, Y . Ye, and S. Xia, “Spatio-temporal gating- adjacency gcn for human motion prediction,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6447–6456

    2022

  16. [24]

    Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction,

    L. Dang, Y . Nie, C. Long, Q. Zhang, and G. Li, “Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 11 467–11 476

    2021

  17. [25]

    Space-time- separable graph convolutional network for pose forecasting,

    T. Sofianos, A. Sampieri, L. Franco, and F. Galasso, “Space-time- separable graph convolutional network for pose forecasting,” inPro- ceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 11 209–11 218

    2021

  18. [26]

    Learning trajectory depen- dencies for human motion prediction,

    W. Mao, M. Liu, M. Salzmann, and H. Li, “Learning trajectory depen- dencies for human motion prediction,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 9489–9497

    2019

  19. [27]

    Progressively generating better initial guesses towards next stages for high-quality human motion prediction,

    T. Ma, Y . Nie, C. Long, Q. Zhang, and G. Li, “Progressively generating better initial guesses towards next stages for high-quality human motion prediction,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 6437–6446

    2022

  20. [28]

    Skeleton- parted graph scattering networks for 3d human motion prediction,

    M. Li, S. Chen, Z. Zhang, L. Xie, Q. Tian, and Y . Zhang, “Skeleton- parted graph scattering networks for 3d human motion prediction,” in European conference on computer vision. Springer, 2022, pp. 18–36

    2022

  21. [29]

    Ksof: Leveraging kinematics and spatio-temporal optimal fusion for human motion prediction,

    R. Ding, K. Qu, and J. Tang, “Ksof: Leveraging kinematics and spatio-temporal optimal fusion for human motion prediction,”Pattern Recognition, vol. 161, p. 111206, 2025

    2025

  22. [30]

    Skeleton-aware representation of spatio-temporal kinematics for 3d human motion prediction,

    S. Du, Z. Zhuang, Z. Wang, Y . Li, and T. Ikenaga, “Skeleton-aware representation of spatio-temporal kinematics for 3d human motion prediction,”IEEE Transactions on Automation Science and Engineering, 2025

    2025

  23. [31]

    Learning progressive joint propagation for human motion prediction,

    Y . Cai, L. Huang, Y . Wang, T.-J. Cham, J. Cai, J. Yuan, J. Liu, X. Yang, Y . Zhu, X. Shenet al., “Learning progressive joint propagation for human motion prediction,” inComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI...

    2020

  24. [32]

    Collaborative multi- dynamic pattern modeling for human motion prediction,

    J. Tang, J. Zhang, R. Ding, B. Gu, and J. Yin, “Collaborative multi- dynamic pattern modeling for human motion prediction,”IEEE Trans- actions on Circuits and Systems for Video Technology, vol. 33, no. 8, pp. 3689–3700, 2023

    2023

  25. [33]

    E (n) equivariant graph neural networks,

    V . G. Satorras, E. Hoogeboom, and M. Welling, “E (n) equivariant graph neural networks,” inInternational conference on machine learning. PMLR, 2021, pp. 9323–9332

    2021

  26. [34]

    Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning,

    C. Xu, R. T. Tan, Y . Tan, S. Chen, Y . G. Wang, X. Wang, and Y . Wang, “Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 1410–1420

    2023

  27. [35]

    Equivariant graph mechanics networks with constraints,

    W. Huang, J. Han, Y . Rong, T. Xu, F. Sun, and J. Huang, “Equivariant graph mechanics networks with constraints,” inInternational Conference on Learning Representations, 2022

    2022

  28. [36]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”Advances in neural information processing systems, vol. 30, 2017

    2017

  29. [37]

    Style machines,

    M. Brand and A. Hertzmann, “Style machines,” inProceedings of the 27th annual conference on Computer graphics and interactive techniques, 2000, pp. 183–192

    2000

  30. [38]

    Efficient nonlinear markov models for human motion,

    A. M. Lehrmann, P. V . Gehler, and S. Nowozin, “Efficient nonlinear markov models for human motion,” inProceedings of the IEEE confer- ence on computer vision and pattern recognition, 2014, pp. 1314–1321

    2014

  31. [39]

    Factored conditional restricted boltz- mann machines for modeling motion style,

    G. W. Taylor and G. E. Hinton, “Factored conditional restricted boltz- mann machines for modeling motion style,” inProceedings of the 26th annual international conference on machine learning, 2009, pp. 1025– 1032

    2009

  32. [40]

    Adversarial geometry-aware human motion prediction,

    L.-Y . Gui, Y .-X. Wang, X. Liang, and J. M. F. Moura, “Adversarial geometry-aware human motion prediction,” inProceedings of the Euro- pean Conference on Computer Vision (ECCV), September 2018

    2018

  33. [41]

    Convolutional sequence to sequence model for human dynamics,

    C. Li, Z. Zhang, W. S. Lee, and G. H. Lee, “Convolutional sequence to sequence model for human dynamics,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5226– 5234

    2018

  34. [42]

    Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction,

    X. Liu, J. Yin, J. Liu, P. Ding, J. Liu, and H. Liu, “Trajectorycnn: a new spatio-temporal feature learning network for human motion prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 6, pp. 2133–2146, 2020

    2020

  35. [43]

    Sta-cnn: Convolutional spatial-temporal attention learning for action recognition,

    H. Yang, C. Yuan, L. Zhang, Y . Sun, W. Hu, and S. J. Maybank, “Sta-cnn: Convolutional spatial-temporal attention learning for action recognition,”IEEE Transactions on Image Processing, vol. 29, pp. 5783– 5793, 2020

    2020

  36. [44]

    Kd-former: Kinematic and dynamic coupled transformer network for 3d human motion prediction,

    J. Dai, H. Li, R. Zeng, J. Bai, F. Zhou, and J. Pan, “Kd-former: Kinematic and dynamic coupled transformer network for 3d human motion prediction,”Pattern Recognition, vol. 143, p. 109806, 2023

    2023

  37. [45]

    Back to mlp: A simple baseline for human motion prediction,

    W. Guo, Y . Du, X. Shen, V . Lepetit, X. Alameda-Pineda, and F. Moreno- Noguer, “Back to mlp: A simple baseline for human motion prediction,” inProceedings of the IEEE/CVF winter conference on applications of computer vision, 2023, pp. 4809–4819

    2023

  38. [46]

    Pose-transformed equivariant network for 3d point trajectory prediction,

    R. Yu and J. Sun, “Pose-transformed equivariant network for 3d point trajectory prediction,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5503–5512

    2024

  39. [47]

    Se (3)-transformers: 3d roto-translation equivariant attention networks,

    F. Fuchs, D. Worrall, V . Fischer, and M. Welling, “Se (3)-transformers: 3d roto-translation equivariant attention networks,”Advances in neural information processing systems, vol. 33, pp. 1970–1981, 2020

    1970

  40. [48]

    Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,

    C. Ionescu, D. Papava, V . Olaru, and C. Sminchisescu, “Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,”IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 7, pp. 1325–1339, 2013

    2013

  41. [49]

    Carnegie Mellon University

    Cmu graphics lab motion capture database. Carnegie Mellon University. [Online]. Available: http://mocap.cs.cmu.edu/

  42. [50]

    Recovering accurate 3d human pose in the wild using imus and a moving camera,

    T. V on Marcard, R. Henschel, M. J. Black, B. Rosenhahn, and G. Pons- Moll, “Recovering accurate 3d human pose in the wild using imus and a moving camera,” inProceedings of the European conference on computer vision (ECCV), 2018, pp. 601–617

    2018

Pith tools

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