fill-vertical

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

1. ffprobe for source w/h/fps/duration.
2. Scene-cut detect via ffmpeg select=gt(scene,thresh) on a downscaled decode → shot list.
3. 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.
4. 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.