- effectcraft-segment: a FaceModel interface (find a face in a region, follow it; a Topology
maps the model's points to the tracker's landmarks and outline), a TensorFlow Lite reader and
interpreter (float ops, float16 weights folded; matches ai-edge-litert to ~1e-6), and
MediaPipe Face Landmarker (Google, Apache-2.0: BlazeFace + Face Mesh V2, 3.8 MB, pinned
SHA-256). The registry gains a task and authors per model; installed weights get a NOTICE.
- effectcraft-track: FaceTracker::new_with uses the model, falls back to the classical fitter
when it finds no face; chin and jaw come from the outline either way so measurements agree.
- Engine: roto_models becomes models (both tasks); face.models / face.model.* commands,
Settings ▸ Face Tracking, `wait` on select/install/download for scripts; track.mask reports
faceModel.
- Credits: ATTRIBUTION.md lists the optional models and the Apache-2.0 specs the code follows.
- Docs: README, ROADMAP (G7), gaps, parity (TRK-3 score unchanged pending G1), agents,
preferences, architecture, segment README.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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