mlsanityData to 14 Oct 2026

Improved Trajectory Reconstruction for Markerless Pose Estimation

arXiv 2303.02413 ↗First posted 4 Mar 2023cs.CVcited by 12

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Abstract (arXiv)

Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system from a heterogeneous sample of 25 individuals seen in a rehabilitation hospital. We found that using a top-down keypoint detector and reconstructing trajectories with an implicit function enabled accurate, smooth and anatomically plausible trajectories, with a noise in the step width estimates compared to a GaitRite walkway of only 8mm.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 4 Mar 2023.

Builds on

14 of 41 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  4. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  5. 2020Deep Learning-Based Human Pose Estimation: A Surveycited by 207
  6. 2022AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Timecited by 200
  7. 2020Whole-Body Human Pose Estimation in the Wildcited by 178
  8. 2021Direct Multi-view Multi-person 3D Pose Estimationcited by 64
  9. 2022Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formatscited by 50
  10. 2020MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimationcited by 35
  11. 2021Shape-aware Multi-Person Pose Estimation from Multi-View Imagescited by 24
  12. 2022PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Researchcited by 10
2 more
  1. 2022On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimationcited by 10
  2. 2022Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributionscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 1; 2026: 5.

12 papers.

  1. 2024Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capturecited by 18
  2. 2023Markerless Motion Capture and Biomechanical Analysis Pipelinecited by 10
  3. 2024Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematicscited by 6
  4. 2023Self-Supervised Learning of Gait-Based Biomarkerscited by 5
  5. 2026Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Modelscited by 5
  6. 2026Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capturecited by 4
  7. 2025Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capturecited by 3
  8. 2024Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulationcited by 2
  9. 2024A Causal Framework for Precision Rehabilitationcited by 2
  10. 2026Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infantscited by 1
  11. 2026BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agentscited by 0
  12. 2026Cross-Model Distillation of a Human-Pose Foundation Model from Unannotated Infant Video for Markerless 3D Pose Estimationcited by 0