| name | fill-vertical |
| description | Reframe a horizontal (16:9) clip to 9:16 (1080x1920) by punching in to FILL the frame — never letterbox, no blur bars. Detects scene cuts and computes ONE static crop box per shot. Clusters face signatures across all shots to find the dominant speaker (storyteller); on multi-face shots it picks the speaker by lip-activity biased toward that identity. A non-speaking reaction/listener shot is framed LOOSER so the short never dwells hero-framed on the wrong person. When FaceLandmarker finds no face it falls to a PERSON tier (MediaPipe Pose) — an action/establishing shot with a human is framed on that person (head on the upper third, punched in) instead of stranding them in dead space; only genuinely person-free footage falls through to the OpenCV-saliency cover crop. Faces are framed ~45% of frame height with the eyeline on the upper third, capped at ~2x upscale. Replaces fit-vertical in the canonical chain. |
fill-vertical
Punch-in 9:16 reframe. Fills 1080x1920 on every shot — the "content" look, not the
blurred-letterbox "webinar" look. Runs after tighten-pace, before
chunk-captions/burn-subtitles.
Usage
.claude/skills/fill-vertical/fill-vertical.sh <input> <out> \
[target=1080x1920] [face_frac=0.45] [max_zoom=2.0] [scene_thresh=0.4] [samples=5]
- input — cut/trimmed/tightened clip (any aspect; designed for 16:9).
- out — written 1080x1920 mp4, audio stream-copied, duration unchanged,
+faststart.
Contract
- In: one video file.
- Out: one 1080x1920 mp4. Audio copied from input. Duration preserved.
Side artifact
<out>.fillplan.json records per-shot crop boxes.
- Idempotent: if
out is newer than input, exits as a cache hit.
How it works
ffprobe for source w/h/fps/duration.- Scene-cut detect via ffmpeg
select=gt(scene,thresh) on a downscaled decode → shot list.
- Per shot: sample
samples frames, run MediaPipe FaceLandmarker (full lip mesh).
- Link faces across frames by center proximity → tracks.
- Speaker = track with max mouth-openness variance. No measurable lip motion →
biggest + most-central face. Single face → trivial.
- Crop box: chosen face ~
face_frac of frame height, eyeline on the upper third,
centered on the face, clamped to source bounds, capped at max_zoom.
- No face → PERSON tier: run MediaPipe Pose (
pose_landmarker.task, created
lazily — only when a shot has no face). If a prominent person is found (visible
across the shot's samples, ≥0.12 of frame tall), frame on them like a face — head
on the upper third, sized from the body's vertical extent to ~62% of crop, punched
in self-limited by max_zoom (kind="person").
- No person → OpenCV
StaticSaliencyFineGrained; crop toward the saliency centroid,
cover-zoom only (no artificial upscale). True scenery (drone, macro) stays here.
- Render each shot (cut → crop → scale → 1080x1920, video only), concat-demuxer join,
stream-copy the original audio over the whole clip. Encodes via
_lib/encode.sh
(respects its VideoToolbox/thread caps — the CPU-brick fix from shorts-xv5).
Tunables
| arg | default | meaning |
|---|
target | 1080x1920 | output WxH |
face_frac | 0.45 | target face height as fraction of frame |
max_zoom | 2.0 | max upscale; past it, accept a smaller-but-sharp face |
scene_thresh | 0.4 | ffmpeg scene-change sensitivity (lower = more cuts) |
samples | 5 | frames sampled per shot for detection (K-frame cap, never per-frame) |
Deps
mediapipe, opencv-contrib-python, numpy (python3). Models bundled under models/:
face_landmarker.task (FaceLandmarker) + pose_landmarker.task (Pose, person tier).
Out of scope
Continuous/tracking pan, split-screen, audio diarization, blur bars/letterbox,
slide-specific handling. Other skills own segment selection, captions, titles, CTA, music.
