Research and tutorials on human pose estimation.
Can a smartphone app really measure how you move? Five motion-capture apps — ViMAS, KinesioCapture, Coach’s Eye, LGait, and SmartGait — have been validated against VICON and 3D systems. Here’s the accuracy each study actually found, app by app.
Can your phone measure how you move accurately enough to fix your form? Here’s why Apple’s ARKit — 3D motion capture from ordinary 2D video, auto-calibrated and real-time — is a promising answer, and how it compares with VICON, 2D video, and Kinect.
We benchmarked pose estimation accuracy across six models — MediaPipe, MeTRAbs, YOLO and MoveNet — against the VICON gold standard. Two clear tiers emerged; here’s which to use, and when.
Can markerless pose estimation replace marker-based motion capture in sports? Our validation study found a ~10-degree mean joint-angle difference — invisible to the eye, but too much for clinical precision. Here’s what that means.
A practical getting-started guide to two free markerless motion-capture tools: OpenPose (install the demo, fix the models, run it) and OpenCap (2 iOS cameras, calibration, and 3D capture).
Part 5 (finale): kinetic/dynamic simulation with OpenSim Moco and automatic differentiation – muscle activations and ground reaction forces from the reconstructed motion.
Part 4: driving an OpenSim model with the reconstructed motion – convert to .trc, LSTM marker augmentation (OpenCap), scale the model, and run inverse kinematics.
Part 3: triangulating 2D keypoints into 3D with the DLT, weighting by OpenPose confidence, person tracking, residual-analysis filtering, and reprojection-error optimization.
Part 2: fixing lens distortion with MATLAB Camera Calibrator, camera intrinsics/extrinsics, the projection matrix (DLT + calibration cage, 12 cameras), and bundle adjustment.
Part 1 of a 5-part series: install OpenPose, fetch the models, fix the common VC++ error, and run it from the command line to get 2D pose data.