Mastering Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim

A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim

Pose2Sim provides a comprehensive Python-based framework for markerless 3D human motion analysis. The pipeline integrates RTMPose for high-performance 2D keypoint detection and leverages OpenSim for biomechanical modeling. This system can predict 47 virtual markers using a Stanford-trained LSTM to enhance kinematic accuracy.

Why This Matters

Traditional motion capture relies on physical markers, which are intrusive and limit ecological validity in real-world environments. Markerless solutions like Pose2Sim address this by using computer vision to triangulate 3D coordinates from standard video feeds, significantly reducing setup complexity and cost. However, the technical reality requires robust handling of reprojection errors and multi-camera synchronization to match the precision of laboratory-grade systems. Filtering methods such as Butterworth or Kalman are essential to mitigate noise inherent in computer vision detections, bridging the gap between raw pixel data and reliable biomechanical joint angles.

Key Insights

• RTMPose utilizes YOLOX detection and HALPE_26 keypoints for balanced 2D pose estimation (Pose2Sim 2022).
• Triangulation weights 2D keypoints by detection confidence scores to minimize reprojection errors.
• Marker augmentation uses a Stanford-trained LSTM to predict 47 virtual markers from sparse 3D keypoints.
• OpenSim integration enables automatic model scaling and Inverse Kinematics (IK) for 3D joint angle computation.
• Synchronization aligns camera frames by correlating vertical keypoint speeds when hardware triggers are unavailable.

Working Examples

Full Pose2Sim pipeline execution from camera calibration to OpenSim kinematics.

import toml\nfrom Pose2Sim import Pose2Sim\n\n# Configure for headless execution\nconfig = toml.load('Config.toml')\nconfig['pose']['display_detection'] = False\nconfig['filtering']['display_figures'] = False\nwith open('Config.toml', 'w') as f:\n    toml.dump(config, f)\n\n# Run full pipeline\nPose2Sim.calibration()\nPose2Sim.poseEstimation()\nPose2Sim.synchronization()\nPose2Sim.personAssociation()\nPose2Sim.triangulation()\nPose2Sim.filtering()\nPose2Sim.markerAugmentation()\nPose2Sim.kinematics()

Practical Applications

• Biomechanical Research: Using Pose2Sim to analyze gait without physical markers. Pitfall: Neglecting camera synchronization leads to inaccurate triangulation and temporal jitter.
• Sports Performance: Tracking athlete joint angles in-field via multi-camera setups. Pitfall: Low detection frequency settings in Config.toml can miss high-velocity movements.
• Clinical Assessment: Implementing markerless scaling in OpenSim for patient-specific models. Pitfall: Poor marker augmentation results can degrade IK accuracy if base keypoints are noisy.

References:

• https://github.com/perfanalytics/pose2sim
• https://discord.com/invite/4mXUdSFjmt
• https://perfanalytics.github.io/pose2sim/