- 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>
parity.md's ≈99% measures breadth only. docs/gaps.md assesses real-world
readiness by dimension (fidelity, .aep interop, stability, platform
reliability, performance, media, AI tools, maturity) with evidence, and
sets prioritised workstreams with done criteria. ROADMAP, README, parity.md,
AGENTS.md and CLAUDE.md point agents at it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
bflatastic
Brandon Thomas