---
license: apache-2.0
library_name: sceneworks
tags:
- face-landmarks
- mediapipe
- facemesh
---

# MediaPipe FaceMesh v2 (SceneWorks native rehost)

Native safetensors rehost of the MediaPipe FaceMesh v2 face-landmark detector, for SceneWorks' MLX and Candle training backends (frozen, differentiable face-landmark loss for character LoRA training).

## Provenance
- Source: [py-feat/mp_facemesh_v2](https://huggingface.co/py-feat/mp_facemesh_v2) at revision `39eb85054cf76fe0f57b7e12d6765ae89d89f2b5`, file `face_landmarks_detector_Nx3x256x256_onnx.pth` (an onnx2torch GraphModule of Google's MediaPipe FaceMesh v2 landmark model).
- License: Apache-2.0, as stated by the source repository.
- Converted with `crates/media/mlx-gen/tools/convert_mp_facemesh_v2.py` (SceneWorks inference repo, commit `1d87a8eb2`), which lowers the FX graph to an "fx-program" stored in the safetensors metadata (`program`) alongside the 281 parameter tensors.

## Verification
- Self-check (lowered program re-executed against the torch module): max abs error 0.0.
- Native executors vs torch on a fixed seeded input: MLX max abs 3.09e-4, Candle max abs 2.86e-4 (output magnitude ~216, ≈1.4e-6 relative).
- Input: N×3×256×256; output0: N×1×1×1434 (478 landmarks × 3).

`face_landmarks_detector.safetensors` sha256: `94e0dda4bb58733fc7454997868232bdcc2b11531a18d2bbfec5479c75f574f4`