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arxiv: 2605.20850 · v1 · pith:AO5WRD5Nnew · submitted 2026-05-20 · 💻 cs.RO

SmoCap: Unified Scale-Pose Canonicalization with Proxy-Mapped Trust-Region QP

Shihao Li , Naohiko Sugita This is my paper

Pith reviewed 2026-05-21 04:37 UTC · model grok-4.3

classification 💻 cs.RO
keywords motion capturescale estimationinverse kinematicstrust-region optimizationproxy mappingcanonicalizationfluoroscopyanthropometry
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The pith

SmoCap jointly estimates morphology and posture in a unified trust-region QP to prevent compensation artifacts in marker-based motion capture.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SmoCap to address the problem of morphology and posture compensating for each other when scaling and inverse kinematics are performed in separate stages. This separation can produce solutions that fit the markers numerically but do not reflect true anatomy or coordinated movement, particularly in directions with fewer observations. SmoCap instead solves for both scale and pose together inside each local quadratic program using a low-dimensional proxy map that couples the variables. The approach is shown to match fluoroscopy measurements with low error and to reduce inconsistencies compared to independent scaling methods. A sympathetic reader would care because reliable canonicalization of motion data is essential for building accurate models in biomechanics, animation, and robotics.

Core claim

SmoCap is a leakage-resistant framework that performs joint scale and pose canonicalization by solving a constrained trust-region quadratic program at each step, using analytical Jacobians derived from a proxy map in a sparse control subspace. This joint estimation in a unified optimization avoids the morphology-posture compensation that arises in stage-wise pipelines.

What carries the argument

The proxy-mapped trust-region quadratic program, which uses a low-dimensional proxy map to couple pose and scale parameters and stabilize weakly observed directions during joint optimization.

If this is right

  • Validated knee flexion angles match fluoroscopy ground truth with an RMSE of 2.9 degrees.
  • Anthropometric endpoint errors average around 3 percent when compared to measured body dimensions.
  • Compared to segment-wise scaling, the method reduces marker RMSE, flexion-extension error, and anthropometric errors in leakage tests.
  • Spine motion remains expressive and coordinated in extreme poses with only a small increase in fitting error of 0.14 mm.
  • Processing completes in 0.2 to 0.3 milliseconds per frame using only two or three iterations per solve.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Applying the same proxy coupling to multi-view or depth-sensor inputs could further improve robustness in uncontrolled environments.
  • The framework might serve as a post-processing step for existing motion capture datasets to retroactively reduce scale-pose leakage.
  • Choosing the proxy dimension based on observed data sparsity could adapt the method to different capture setups without manual adjustment.
  • Extension to animal or non-humanoid models would test whether the coordinated structure assumption holds beyond standard human skeletons.

Load-bearing premise

That mapping the optimization variables through a low-dimensional proxy preserves the ability to fit observed markers accurately while enforcing coordination and stabilizing weak directions.

What would settle it

Running the method on a dataset of subjects with highly asymmetric body proportions or in movements where individual vertebral segments move independently, and checking if the anthropometric endpoint error exceeds that of a segment-wise scaling baseline.

Figures

Figures reproduced from arXiv: 2605.20850 by Naohiko Sugita, Shihao Li.

Figure 1
Figure 1. Figure 1: Representative outputs of SmoCap motion canonicalization. Motion and scale reconstruction: (a) human gait, (b) yoga poses, (c) proxy-mapped spine motion, and (d) quadruped motion examples. The quadruped examples use public dog motion capture and PFERD horse motion data [29, 30]. IK solves [14–16], while separated scale and pose parameter updates can still leave residual trade-offs. A simultaneous up￾dating… view at source ↗
Figure 2
Figure 2. Figure 2: Design rationale under mixed determinacy. (a) Stage-wise updates can repeatedly assign residuals to pose or scale, whereas unified local scale–pose updates resolve both components simultane￾ously. (b) Under weak observability, independent high-DoF controls can produce incoherent internal motion, whereas proxy-coupled low dimensional controls coordinate structured DoFs. anatomical structures of expressive m… view at source ↗
Figure 3
Figure 3. Figure 3: Fluoroscopy-based knee kinematics validation. SmoCap predicted knee flexion trajectories are compared with fluoroscopy￾derived references across gait, chair sit-to-stand, squat, ramp descent, and stair descent. Trajectories are shown over normalized cycles, with shaded regions showing cross-trial variation. For throughput and convergence statistics, we report per￾frame and dataset level runtime, together w… view at source ↗
Figure 4
Figure 4. Figure 4: Anthropometric endpoint error distributions for external morphology validation. Signed endpoint errors are shown for length-like and width-like metrics on CAMS-Knee and Riglet datasets. Violin distributions summarize endpoint errors, and median absolute percentage errors are reported below. nated spine structures can only be constrained indirectly, which creates a weakly observed setting. We compare three … view at source ↗
Figure 5
Figure 5. Figure 5: A shows the temporal roughness of per joint angle history, and spatial roughness of spine at each time instant. Poly exhibits lower roughness overall. In contrast, nopoly formed a separated high-roughness cluster, whereas classical was temporally smooth but spatially rougher. Fig. 5B reports relative marker fitting error of poly and nopoly against classical. Poly is close to classical (median ∆RMSE = +0.14… view at source ↗
Figure 6
Figure 6. Figure 6: Spine bending patterns under different spine configurations. Top: spine curves at five sampled time points. Bottom: skeleton render at a representative frame. The proxy-coupled spine (poly) shows smooth distributed curvature, the independent-DoF spine (nopoly) shows fragmented local bending, and the segmented spine (classical) shows piecewise rigid bending [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically inconsistent yet numerically acceptable solutions, especially in weakly observed directions. We present SmoCap, a leakage-resistant canonicalization framework that estimates morphology and posture jointly in each local trust-region quadratic program (QP) within a sparse control subspace. Methods: SmoCap solves a constrained trust-region QP with analytical proxy-mapped pose and scale Jacobians. The low dimensional proxy map stabilizes weakly observed directions and drives coordinated structures. An optional pre-solve provides warm starts in difficult configurations. The framework is evaluated using cohort fluoroscopy knee motion, anthropometric ground truth, and extreme yoga sequences. Results: SmoCap achieved 2.9 degree knee flexion RMSE against fluoroscopy, and a pooled anthropometric endpoint error around 3%. In the leakage audit against segment wise scaling, SmoCap also reduced marker RMSE, FE error, and anthropometric endpoint error. Proxy coupling preserved expressive and coordinated spine motion with marginal fitting error increase (+0.14 mm, +0.6%) against baseline models in yoga ablation. Median marker RMSE was around 20 mm, and median runtime was 0.204-0.332 ms/frame, achieved with consistently 2-3 iterations. Conclusion: SmoCap provides an externally validated unified coupling-aware scale-pose framework, making externally consistent motion canonicalization practical at dataset scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents SmoCap, a leakage-resistant canonicalization framework for joint morphology and posture estimation in motion capture. It formulates the problem as a constrained trust-region QP solved with analytical proxy-mapped pose and scale Jacobians in a sparse control subspace, optionally with a pre-solve warm start. The method is evaluated on cohort fluoroscopy knee motion, anthropometric ground truth, and extreme yoga sequences, reporting 2.9° knee flexion RMSE against fluoroscopy, ~3% pooled anthropometric endpoint error, reduced marker/FE/anthropometric errors versus segment-wise scaling baselines, and preservation of expressive spine motion with only marginal error increase (+0.14 mm marker RMSE, +0.6%) in yoga ablation. Median runtime is 0.204–0.332 ms/frame with 2–3 iterations.

Significance. If the proxy-map construction and stabilization properties hold, the work offers a practical, externally validated alternative to stage-wise scaling-plus-IK pipelines that can reduce morphology-posture compensation artifacts at dataset scale while maintaining real-time performance. The explicit external validation against fluoroscopy and anthropometrics, together with the reported runtime and iteration counts, strengthens the case for adoption in large-scale motion capture pipelines.

major comments (2)
  1. [Results] Results (yoga ablation paragraph): the claim that 'proxy coupling preserved expressive and coordinated spine motion' rests on aggregate marker RMSE and anthropometric endpoint error increases of +0.14 mm and +0.6%. These metrics do not directly measure spine range of motion, inter-segment angle coordination, or effective rank reduction in the spine subspace; a direct test (e.g., comparison of spine flexion/extension histograms or Jacobian singular values) is needed to confirm the low-dimensional proxy map does not bias toward under-expressive solutions.
  2. [Methods] Methods (description of proxy-mapped Jacobians): the abstract and results invoke 'analytical proxy-mapped pose and scale Jacobians' and a 'low dimensional proxy map' that 'stabilizes weakly observed directions,' yet no explicit construction of the proxy map, its dimensionality, or the derivation of the mapped Jacobians is provided. This detail is load-bearing for the central claim that the QP avoids new fitting artifacts while driving coordinated structures.
minor comments (2)
  1. [Abstract] Abstract and results: report error bars or standard deviations alongside the 2.9° RMSE, 3% endpoint error, and median runtime figures to allow assessment of variability across subjects and trials.
  2. [Results] Results: clarify the exact definition of 'pooled anthropometric endpoint error' and how the leakage audit against segment-wise scaling was performed (e.g., which segments, which scaling parameters held fixed).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. We address each major comment below and have revised the manuscript to incorporate the suggested improvements where they strengthen the presentation and support for our claims.

read point-by-point responses

  1. Referee: [Results] Results (yoga ablation paragraph): the claim that 'proxy coupling preserved expressive and coordinated spine motion' rests on aggregate marker RMSE and anthropometric endpoint error increases of +0.14 mm and +0.6%. These metrics do not directly measure spine range of motion, inter-segment angle coordination, or effective rank reduction in the spine subspace; a direct test (e.g., comparison of spine flexion/extension histograms or Jacobian singular values) is needed to confirm the low-dimensional proxy map does not bias toward under-expressive solutions.

    Authors: We agree that the original metrics provide only indirect evidence for preservation of expressive spine motion. In the revised manuscript we have added direct supporting analyses in the yoga ablation subsection: histograms comparing spine flexion/extension distributions between the proxy-mapped and baseline solutions, together with the singular values of the spine-subspace Jacobian to quantify effective rank. These additions confirm that the low-dimensional proxy does not induce under-expressiveness while still delivering the reported marginal error increase. revision: yes

  2. Referee: [Methods] Methods (description of proxy-mapped Jacobians): the abstract and results invoke 'analytical proxy-mapped pose and scale Jacobians' and a 'low dimensional proxy map' that 'stabilizes weakly observed directions,' yet no explicit construction of the proxy map, its dimensionality, or the derivation of the mapped Jacobians is provided. This detail is load-bearing for the central claim that the QP avoids new fitting artifacts while driving coordinated structures.

    Authors: The referee correctly notes that the construction details were insufficiently explicit. We have expanded the Methods section with a dedicated subsection that (i) defines the proxy map as a sparse linear selection matrix of dimension 18 (9 pose + 9 scale controls) derived from principal modes of coordinated skeletal motion, (ii) gives the closed-form derivation of the analytical proxy-mapped Jacobians as the composition of the standard kinematic Jacobian with the proxy matrix, and (iii) includes pseudocode for map construction and the resulting QP. These additions make the stabilization mechanism fully reproducible and directly support the central claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; external validation against fluoroscopy and anthropometrics

full rationale

The paper describes a trust-region QP solver using analytical proxy-mapped Jacobians for joint scale-pose estimation. Performance metrics such as 2.9° knee flexion RMSE and ~3% anthropometric endpoint error are reported against independent external references (fluoroscopy ground truth and anthropometric measurements), not derived from or equivalent to the fitted parameters themselves. The proxy map's stabilization of weakly observed directions is presented as a design property with empirical ablation support (e.g., marginal +0.14 mm RMSE increase), but these are measured outcomes rather than tautological redefinitions. No equations reduce by construction to inputs, no self-citation chains bear the central claim, and no uniqueness theorems or ansatzes are smuggled in. The framework is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract supplies no explicit free parameters, axioms, or invented entities; the proxy map and trust-region formulation are treated as engineering choices whose details are not disclosed.

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Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    SMPL: a skinned multi-person linear model

    M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “SMPL: A skinned multi-person linear model,” ACM Trans. Graph., vol. 34, no. 6, pp. 1–16, 2015, doi: 10.1145/2816795.2818013

    work page doi:10.1145/2816795.2818013 2015

  2. [2]

    AMASS: Archive of motion capture as surface shapes,

    N. Mahmood, N. Ghorbani, N. F. Troje, G. Pons-Moll, and M. J. Black, “AMASS: Archive of motion capture as surface shapes,” inProc. IEEE/CVF Int. Conf. Comput. Vis., 2019, pp. 5442–5451

    work page 2019

  3. [3]

    GroundLink: A dataset unifying human body movement and ground reaction dynamics,

    X. Han, B. Senderling, S. To, D. Kumar, E. Whiting, and J. Saito, “GroundLink: A dataset unifying human body movement and ground reaction dynamics,” inProc. SIGGRAPH Asia Conf. Papers, 2023, pp. 1–10, doi: 10.1145/3610548.3618247

    work page doi:10.1145/3610548.3618247 2023

  4. [4]

    On-field player workload exposure and knee injury risk monitoring via deep learning,

    W. R. Johnson, A. Mian, D. G. Lloyd, and J. A. Alderson, “On-field player workload exposure and knee injury risk monitoring via deep learning,”J. Biomech., vol. 93, pp. 185–193, 2019, doi: 10.1016/j.jbiomech.2019.07.002

    work page doi:10.1016/j.jbiomech.2019.07.002 2019

  5. [5]

    Deepmimic: Example-guided deep reinforcement learning of physics-based character skills

    X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne, “DeepMimic: Example-guided deep reinforce- ment learning of physics-based character skills,”ACM Trans. Graph., vol. 37, no. 4, pp. 1–14, 2018, doi: 10.1145/3197517.3201311

    work page doi:10.1145/3197517.3201311 2018

  6. [6]

    In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

    Z. Luo, J. Cao, A. Winkler, K. Kitani, and W. Xu, “Per- petual humanoid control for real-time simulated avatars,” inProc. IEEE/CVF Int. Conf. Comput. Vis., 2023, pp. 10861–10870, doi: 10.1109/ICCV51070.2023.01000

    work page doi:10.1109/iccv51070.2023.01000 2023

  7. [7]

    Design of patient-specific gait modifications for knee osteoarthritis rehabilitation,

    B. J. Fregly, J. A. Reinbolt, K. L. Rooney, K. H. Mitchell, and T. L. Chmielewski, “Design of patient-specific gait modifications for knee osteoarthritis rehabilitation,”IEEE Trans. Biomed. Eng., vol. 54, no. 9, pp. 1687–1695, 2007, doi: 10.1109/TBME.2007.891934

    work page doi:10.1109/tbme.2007.891934 2007

  8. [8]

    Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints,

    T. W. Lu and J. J. O’Connor, “Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints,”J. Biomech., vol. 32, no. 2, pp. 129–134, 1999, doi: 10.1016/S0021-9290(98)00158-4

    work page doi:10.1016/s0021-9290(98)00158-4 1999

  9. [9]

    Kinematic analysis of over-determinate biomechan- ical systems,

    M. S. Andersen, M. Damsgaard, and J. Rasmussen, “Kinematic analysis of over-determinate biomechan- ical systems,”Comput. Methods Biomech. Biomed. Eng., vol. 12, no. 4, pp. 371–384, 2009, doi: 10.1080/10255840802459412

    work page doi:10.1080/10255840802459412 2009

  10. [10]

    Influence of joint constraints on lower limb kinematics estima- tion from skin markers using global optimization,

    S. Duprey, L. Ch `eze, and R. Dumas, “Influence of joint constraints on lower limb kinematics estima- tion from skin markers using global optimization,”J. Biomech., vol. 43, no. 14, pp. 2858–2862, 2010, doi: 10.1016/j.jbiomech.2010.06.010

    work page doi:10.1016/j.jbiomech.2010.06.010 2010

  11. [11]

    Scaling of musculoskeletal models from static and dynamic trials,

    M. E. Lund, M. S. Andersen, M. de Zee, and J. Ras- mussen, “Scaling of musculoskeletal models from static and dynamic trials,”Int. Biomech., vol. 2, no. 1, pp. 1–11, 2015, doi: 10.1080/23335432.2014.993706

    work page doi:10.1080/23335432.2014.993706 2015

  12. [12]

    Shape model constrained scaling improves repeatability of gait data,

    D. Bakke and T. Besier, “Shape model constrained scaling improves repeatability of gait data,”J. Biomech., vol. 107, 2020, Art. no. 109838, doi: 10.1016/j.jbiomech.2020.109838

    work page doi:10.1016/j.jbiomech.2020.109838 2020

  13. [13]

    Shape-model scaling is more robust than linear scaling to marker place- ment error,

    D. Bakke, P. Ortega-Auriol, and T. Besier, “Shape-model scaling is more robust than linear scaling to marker place- ment error,”J. Biomech., vol. 160, 2023, Art. no. 111805, doi: 10.1016/j.jbiomech.2023.111805

    work page doi:10.1016/j.jbiomech.2023.111805 2023

  14. [14]

    Re- peatability of an optimised lower body model,

    I. W. Charlton, P. Tate, P. Smyth, and L. Roren, “Re- peatability of an optimised lower body model,”Gait Posture, vol. 20, no. 2, pp. 213–221, 2004, doi: 10.1016/j.gaitpost.2003.09.004

    work page doi:10.1016/j.gaitpost.2003.09.004 2004

  15. [15]

    Determination of patient-specific multi-joint kinematic models through two-level optimiza- tion,

    J. A. Reinboltet al., “Determination of patient-specific multi-joint kinematic models through two-level optimiza- tion,”J. Biomech., vol. 38, no. 3, pp. 621–626, 2005, doi: 10.1016/j.jbiomech.2004.03.031

    work page doi:10.1016/j.jbiomech.2004.03.031 2005

  16. [16]

    A computational framework to predict post-treatment outcome for gait-related disorders,

    J. A. Reinbolt, R. T. Haftka, T. L. Chmielewski, and B. J. Fregly, “A computational framework to predict post-treatment outcome for gait-related disorders,”Med. Eng. Phys., vol. 30, no. 4, pp. 434–443, 2008, doi: 10.1016/j.medengphy.2007.05.005

    work page doi:10.1016/j.medengphy.2007.05.005 2008

  17. [17]

    A computationally efficient optimisation- based method for parameter identification of kinemat- ically determinate and over-determinate biomechan- ical systems,

    M. S. Andersen, M. Damsgaard, B. MacWilliams, and J. Rasmussen, “A computationally efficient optimisation- based method for parameter identification of kinemat- ically determinate and over-determinate biomechan- ical systems,”Comput. Methods Biomech. Biomed. Eng., vol. 13, no. 2, pp. 171–183, 2010, doi: 10.1080/10255840903067080

    work page doi:10.1080/10255840903067080 2010

  18. [18]

    Analysis of musculoskeletal sys- tems in the AnyBody Modeling System,

    M. Damsgaard, J. Rasmussen, S. T. Christensen, E. Surma, and M. de Zee, “Analysis of musculoskeletal sys- tems in the AnyBody Modeling System,”Simul. Model. Pract. Theory, vol. 14, no. 8, pp. 1100–1111, 2006, doi: 10.1016/j.simpat.2006.09.001

    work page doi:10.1016/j.simpat.2006.09.001 2006

  19. [19]

    A generic detailed rigid-body lumbar spine model,

    M. de Zee, L. Hansen, C. Wong, J. Rasmussen, and E. B. Simonsen, “A generic detailed rigid-body lumbar spine model,”J. Biomech., vol. 40, no. 6, pp. 1219–1227, 2007, doi: 10.1016/j.jbiomech.2006.05.030

    work page doi:10.1016/j.jbiomech.2006.05.030 2007

  20. [20]

    Hy- brIK: A hybrid analytical-neural inverse kinematics solu- tion for 3D human pose and shape estimation,

    J. Li, C. Xu, Z. Chen, S. Bian, L. Yang, and C. Lu, “Hy- brIK: A hybrid analytical-neural inverse kinematics solu- tion for 3D human pose and shape estimation,” inProc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2021, pp. 3383–3393

    work page 2021

  21. [21]

    MANIKIN: Biomechanically accurate neural inverse kinematics for human motion estimation,

    J. Jiang, P. Streli, X. Luo, C. Gebhardt, and C. Holz, “MANIKIN: Biomechanically accurate neural inverse kinematics for human motion estimation,” inComputer Vision – ECCV 2024, Lecture Notes in Computer Sci- ence, vol. 15060. Cham, Switzerland: Springer, 2025, pp. 128–146, doi: 10.1007/978-3-031-72627-9 8

    work page doi:10.1007/978-3-031-72627-9 2024

  22. [22]

    OpenCap: Human movement dynamics from smartphone videos

    S. D. Uhlrichet al., “OpenCap: Human movement dynamics from smartphone videos,”PLOS Comput. Biol., vol. 19, no. 10, 2023, Art. no. e1011462, doi: 10.1371/journal.pcbi.1011462. 9

    work page doi:10.1371/journal.pcbi.1011462 2023

  23. [23]

    AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization,

    K. Werlinget al., “AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization,” PLOS ONE, vol. 18, no. 11, 2023, Art. no. e0295152, doi: 10.1371/journal.pone.0295152

    work page doi:10.1371/journal.pone.0295152 2023

  24. [24]

    Differentiable biomechanics unlocks op- portunities for markerless motion capture,

    R. J. Cotton, “Differentiable biomechanics unlocks op- portunities for markerless motion capture,” arXiv preprint arXiv:2402.17192, 2024

    work page arXiv 2024

  25. [25]

    Mu- joco: A physics engine for model-based control

    E. Todorov, T. Erez, and Y . Tassa, “MuJoCo: A physics engine for model-based control,” inProc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2012, pp. 5026–5033, doi: 10.1109/IROS.2012.6386109

    work page doi:10.1109/iros.2012.6386109 2012

  26. [26]

    A comprehensive assessment of the musculoskeletal system: The CAMS-Knee data set,

    W. R. Tayloret al., “A comprehensive assessment of the musculoskeletal system: The CAMS-Knee data set,”J. Biomech., vol. 65, pp. 32–39, 2017, doi: 10.1016/j.jbiomech.2017.09.022

    work page doi:10.1016/j.jbiomech.2017.09.022 2017

  27. [27]

    3D motion analysis dataset of healthy young adult volunteers walking and running on over- ground and treadmill,

    L. Rigletet al., “3D motion analysis dataset of healthy young adult volunteers walking and running on over- ground and treadmill,”Sci. Data, vol. 11, no. 1, 2024, Art. no. 556, doi: 10.1038/s41597-024-03420-y

    work page doi:10.1038/s41597-024-03420-y 2024

  28. [28]

    T2m-gpt: Generating human motion from textual descriptions with discrete representations

    S. Tripathi, L. M ¨uller, C.-H. P. Huang, O. Taheri, M. J. Black, and D. Tzionas, “3D human pose estimation via intuitive physics,” inProc. IEEE/CVF Conf. Com- put. Vis. Pattern Recognit., 2023, pp. 4713–4725, doi: 10.1109/cvpr52729.2023.00457

    work page doi:10.1109/cvpr52729.2023.00457 2023

  29. [29]

    University of Portsmouth - Dog Mo- tion Capture,

    A. Counsell, “University of Portsmouth - Dog Mo- tion Capture,” YouTube video, Dec. 2, 2016. [Online]. Available: https://www.youtube.com/watch? app=desktop&v=OYh0jBDjNf4. Accessed: Mar. 18, 2024

    work page 2016

  30. [30]

    The Poses for Equine Research Dataset (PFERD),

    C. Liet al., “The Poses for Equine Research Dataset (PFERD),”Sci. Data, vol. 11, no. 1, 2024, Art. no. 497, doi: 10.1038/s41597-024-03312-1

    work page doi:10.1038/s41597-024-03312-1 2024

  31. [31]

    Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement,

    J. L. Hicks, T. K. Uchida, A. Seth, A. Rajagopal, and S. L. Delp, “Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement,”J. Biomech. Eng., vol. 137, no. 2, 2015, Art. no. 020905, doi: 10.1115/1.4029304

    work page doi:10.1115/1.4029304 2015

  32. [32]

    A marker registration method to improve joint angles computed by constrained inverse kinematics,

    J. J. Dunne, T. K. Uchida, T. F. Besier, S. L. Delp, and A. Seth, “A marker registration method to improve joint angles computed by constrained inverse kinematics,” PLOS ONE, vol. 16, no. 5, 2021, Art. no. e0252425, doi: 10.1371/journal.pone.0252425

    work page doi:10.1371/journal.pone.0252425 2021

  33. [33]

    A model-based motion cap- ture marker location refinement approach using inverse kinematics from dynamic trials,

    M. A. Price, A. K. LaPr `e, R. T. Johnson, B. R. Um- berger, and F. C. Sup IV , “A model-based motion cap- ture marker location refinement approach using inverse kinematics from dynamic trials,”Int. J. Numer . Methods Biomed. Eng., vol. 36, no. 1, 2020, Art. no. e3283, doi: 10.1002/cnm.3283

    work page doi:10.1002/cnm.3283 2020

  34. [34]

    A joint coordinate system for the clinical description of three-dimensional motions: Application to the knee,

    E. S. Grood and W. J. Suntay, “A joint coordinate system for the clinical description of three-dimensional motions: Application to the knee,”J. Biomech. Eng., vol. 105, no. 2, pp. 136–144, 1983, doi: 10.1115/1.3138397

    work page doi:10.1115/1.3138397 1983

  35. [35]

    OpenSim: Open-source software to create and an- alyze dynamic simulations of movement,

    S. L. Delp, F. C. Anderson, A. S. Arnold, P. Loan, A. Habib, C. T. John, E. Guendelman, and D. G. The- len, “OpenSim: Open-source software to create and an- alyze dynamic simulations of movement,”IEEE Trans. Biomed. Eng., vol. 54, no. 11, pp. 1940–1950, 2007, doi: 10.1109/TBME.2007.901024

    work page doi:10.1109/tbme.2007.901024 1940

  36. [36]

    Accuracy and relia- bility of marker-based approaches to scale the pelvis, thigh, and shank segments in musculoskeletal models,

    H. Kainz, H. X. Hoang, C. Stockton, R. S. Boyd, D. G. Lloyd, and C. P. Carty, “Accuracy and relia- bility of marker-based approaches to scale the pelvis, thigh, and shank segments in musculoskeletal models,”J. Appl. Biomech., vol. 33, no. 5, pp. 354–360, 2017, doi: 10.1123/jab.2016-0282

    work page doi:10.1123/jab.2016-0282 2017

  37. [37]

    Tibiofemoral and tibiocalcaneal motion during walking: External vs. skeletal markers,

    C. Reinschmidtet al., “Tibiofemoral and tibiocalcaneal motion during walking: External vs. skeletal markers,” Gait Posture, vol. 6, no. 2, pp. 98–109, 1997, doi: 10.1016/S0966-6362(97)01110-7

    work page doi:10.1016/s0966-6362(97)01110-7 1997

  38. [38]

    Simul- taneous measurements of knee motion using an optical tracking system and radiostereometric analysis (RSA),

    R. Tranberg, T. Saari, R. Z¨ugner, and J. K¨arrholm, “Simul- taneous measurements of knee motion using an optical tracking system and radiostereometric analysis (RSA),” Acta Orthop., vol. 82, no. 2, pp. 171–176, 2011, doi: 10.3109/17453674.2011.570675

    work page doi:10.3109/17453674.2011.570675 2011

  39. [39]

    Skin and cluster markers underestimate knee flexion during controlled motions. Evaluation of 12 patients with knee arthroplasty using radiosterometric analysis as reference,

    A. F¨andriks, R. Z ¨ugner, B. Shareghi, J. K ¨arrholm, and R. Tranberg, “Skin and cluster markers underestimate knee flexion during controlled motions. Evaluation of 12 patients with knee arthroplasty using radiosterometric analysis as reference,”J. Biomech., vol. 182, 2025, Art. no. 112591, doi: 10.1016/j.jbiomech.2025.112591

    work page doi:10.1016/j.jbiomech.2025.112591 2025

  40. [40]

    Validation of a portable marker-based motion analysis system,

    S. Wanget al., “Validation of a portable marker-based motion analysis system,”J. Orthop. Surg. Res., vol. 16, no. 1, 2021, Art. no. 425, doi: 10.1186/s13018-021-02576-2

    work page doi:10.1186/s13018-021-02576-2 2021

  41. [41]

    Effect of skin movement artifact on knee kinematics during gait and cutting mo- tions measured in vivo,

    D. L. Benoit, D. K. Ramsey, M. Lamontagne, L. Xu, P. Wretenberg, and P. Renstr¨om, “Effect of skin movement artifact on knee kinematics during gait and cutting mo- tions measured in vivo,”Gait Posture, vol. 24, no. 2, pp. 152–164, 2006, doi: 10.1016/j.gaitpost.2005.04.012

    work page doi:10.1016/j.gaitpost.2005.04.012 2006

  42. [42]

    Kinematic differences between optical motion capture and biplanar videoradiography during a jump- cut maneuver,

    D. L. Miranda, M. J. Rainbow, J. J. Crisco, and B. C. Fleming, “Kinematic differences between optical motion capture and biplanar videoradiography during a jump- cut maneuver,”J. Biomech., vol. 46, no. 3, pp. 567–573, 2013, doi: 10.1016/j.jbiomech.2012.09.023

    work page doi:10.1016/j.jbiomech.2012.09.023 2013

  43. [43]

    The neuromusculoskeletal mod- eling pipeline: MATLAB-based model personalization and treatment optimization functionality for OpenSim,

    C. V . Hammondet al., “The neuromusculoskeletal mod- eling pipeline: MATLAB-based model personalization and treatment optimization functionality for OpenSim,” J. NeuroEng. Rehabil., vol. 22, no. 1, 2025, Art. no. 112, doi: 10.1186/s12984-025-01629-5. 10

    work page doi:10.1186/s12984-025-01629-5 2025

  44. [44]

    Human movement analysis using stereophotogrammetry. Part 3: Soft tissue artifact assessment and compensation,

    A. Leardini, L. Chiari, U. Della Croce, and A. Cappozzo, “Human movement analysis using stereophotogrammetry. Part 3: Soft tissue artifact assessment and compensation,” Gait Posture, vol. 21, no. 2, pp. 212–225, 2005, doi: 10.1016/j.gaitpost.2004.05.002

    work page doi:10.1016/j.gaitpost.2004.05.002 2005

  45. [45]

    Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty,

    A. Barr´e, J.-P. Thiran, B. M. Jolles, N. Theumann, and K. Aminian, “Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty,”IEEE Trans. Biomed. Eng., vol. 60, no. 11, pp. 3131–3140, 2013, doi: 10.1109/TBME.2013.2268938

    work page doi:10.1109/tbme.2013.2268938 2013

  46. [46]

    A linear soft tissue artefact model for human movement analysis: Proof of concept using in vivo data,

    M. S. Andersen, M. Damsgaard, J. Rasmussen, D. K. Ramsey, and D. L. Benoit, “A linear soft tissue artefact model for human movement analysis: Proof of concept using in vivo data,”Gait Posture, vol. 35, no. 4, pp. 606– 611, 2012, doi: 10.1016/j.gaitpost.2011.11.032

    work page doi:10.1016/j.gaitpost.2011.11.032 2012

  47. [47]

    OpenSim Moco: Musculoskeletal optimal control,

    C. L. Dembia, N. A. Bianco, A. Falisse, J. L. Hicks, and S. L. Delp, “OpenSim Moco: Musculoskeletal optimal control,”PLOS Comput. Biol., vol. 16, no. 12, 2020, Art. no. e1008493, doi: 10.1371/journal.pcbi.1008493

    work page doi:10.1371/journal.pcbi.1008493 2020

  48. [48]

    Auto- mated optimization of residual reduction algorithm pa- rameters in OpenSim,

    J. T. Sturdy, A. K. Silverman, and N. T. Pickle, “Auto- mated optimization of residual reduction algorithm pa- rameters in OpenSim,”J. Biomech., vol. 137, 2022, Art. no. 111087, doi: 10.1016/j.jbiomech.2022.111087

    work page doi:10.1016/j.jbiomech.2022.111087 2022

  49. [49]

    Evaluation of a patient-specific cost function to pre- dict the influence of foot path on the knee adduc- tion torque during gait,

    B. J. Fregly, J. A. Reinbolt, and T. L. Chmielewski, “Evaluation of a patient-specific cost function to pre- dict the influence of foot path on the knee adduc- tion torque during gait,”Comput. Methods Biomech. Biomed. Eng., vol. 11, no. 1, pp. 63–71, 2008, doi: 10.1080/10255840701552036

    work page doi:10.1080/10255840701552036 2008

  50. [50]

    Muscle–tendon unit scaling methods of Hill-type muscu- loskeletal models: An overview,

    F. Heinen, M. E. Lund, J. Rasmussen, and M. de Zee, “Muscle–tendon unit scaling methods of Hill-type muscu- loskeletal models: An overview,”Proc. Inst. Mech. Eng. H, J. Eng. Med., vol. 230, no. 10, pp. 976–984, 2016, doi: 10.1177/0954411916659894

    work page doi:10.1177/0954411916659894 2016

  51. [51]

    Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique,

    L. Modenese, E. Ceseracciu, M. Reggiani, and D. G. Lloyd, “Estimation of musculotendon parameters for scaled and subject specific musculoskeletal models using an optimization technique,”J. Biomech., vol. 49, no. 2, pp. 141–148, 2016, doi: 10.1016/j.jbiomech.2015.11.006. 11

    work page doi:10.1016/j.jbiomech.2015.11.006 2016