bflatasticandClaude Opus 5.5 e43bf66775 M13.36: face tracking trained model — MediaPipe Face Landmarker on a pure-Rust TFLite interpreter ...

- 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 <[email protected]>

2026-10-05 20:39:45 +07:00

..

src

M13.36: face tracking trained model — MediaPipe Face Landmarker on a pure-Rust TFLite interpreter

2026-10-05 20:39:45 +07:00

tests/qa

media: WebM VP9 alpha imports as straight alpha (FilmCraft 2c5ba7a)

2026-10-03 23:57:57 -04:00

Cargo.toml

M13.15: qa scenarios 1-4 (kinetic type, shape logo, compositing, tracking) and fixes

2026-10-03 12:09:41 -04:00