---
license: apache-2.0
tags: [onnx, mediapipe, face-landmarks, face-mesh, qtmesheditor]
---

# Face Mesh V2 landmark model (ONNX)

Google MediaPipe's Face Landmarks Detector (Face Mesh V2), converted to ONNX.

- **Input** `[N,256,256,3]` RGB in `[0,1]` — the rotated, cropped face ROI.
- **Outputs** `[N,1,1,1434]` = **478 landmarks × (x,y,z)** in 256-crop pixels,
  plus a face-presence logit. Landmarks project back through the inverse ROI
  transform; a 146-landmark subset feeds the blendshape model.

## License

Apache-2.0 — this graph is a direct ONNX conversion of a Google
[MediaPipe](https://developers.google.com/mediapipe) model (Apache-2.0 code
AND weights). Conversion + numerical-parity proof (vs the Python `mediapipe`
reference): [`scripts/export-facecap-onnx.py`](https://github.com/fernandotonon/QtMeshEditor/blob/master/scripts/export-facecap-onnx.py),
contract in [`docs/MOCAP_SPIKE.md`](https://github.com/fernandotonon/QtMeshEditor/blob/master/docs/MOCAP_SPIKE.md).

## How it is used

Mirror of one graph from [`fernandotonon/QtMeshEditor-models`](https://huggingface.co/fernandotonon/QtMeshEditor-models)
(`mocap/…`), which [QtMeshEditor](https://github.com/fernandotonon/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.