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fernandotonon/QtMeshEditor-poselandmarks-onnx
QtMeshEditor-poselandmarks-onnx is a machine learning model from fernandotonon. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Google MediaPipe's Pose Landmarks Detector (BlazePose full), converted to ONNX.
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Updated Jul 13, 2026
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From the Hugging Face model README
Google MediaPipe's Pose Landmarks Detector (BlazePose full), converted to ONNX.
[1,256,256,3] RGB in [0,1] — the rotated, cropped person ROI.[1,195] = 39 × (x,y,z,visibility,presence) screen landmarks
in 256-crop pixels; [1,1] pose-presence probability; [1,256,256,1]
segmentation; [1,64,64,39] heatmap; [1,117] = 39 × (x,y,z) WORLD
landmarks in metres, hip-centred (the input to the analytic body-pose /
IK solver — landmarks 0–32 are the 33 real body joints).Apache-2.0 — this graph is a direct ONNX conversion of a Google
MediaPipe model (Apache-2.0 code
AND weights). Conversion + numerical-parity proof (vs the Python mediapipe
reference): scripts/export-facecap-onnx.py,
contract in docs/MOCAP_SPIKE.md.
Mirror of one graph from fernandotonon/QtMeshEditor-models
(mocap/…), which QtMeshEditor
downloads on first use for its Performance Capture feature (video/webcam →
facial morph + head + full-body skeletal animation, epic #869). This standalone
repo is for discoverability; the app fetches from the aggregate repo.