name: wjs-reframing-video description: Use when the user wants to convert a video between horizontal and vertical orientations while preserving the inverted aspect ratio (16:9 ↔ 9:16, 4:3 ↔ 3:4, 21:9 ↔ 9:21). The skill crops a narrow band from the source and tracks the active speaker — the person whose mouth is moving — via MediaPipe face landmarks and mouth-aspect-ratio variance, so the talker stays in frame even when other people are visible. Triggers — "横转竖", "竖转横", "做成竖屏发抖音/视频号/小红书", "16:9 to 9:16", "make this vertical for Reels / TikTok / YouTube Shorts", "crop to portrait", "convert to landscape".

wjs-reframing-video

Convert a video's orientation by cropping a narrow band from the source — not by physically rotating it. The crop window follows the active speaker (the face whose mouth is moving), not just the largest or most-confident face. A

.crop.json

sidecar records the crop plan, the per-segment speaker decisions, and the parameters used. The original input is never modified.

When to use

Repurposing a 16:9 podcast / interview / talk for vertical short-video platforms (WeChat Channels 视频号, Douyin 抖音, Xiaohongshu 小红书, YouTube Shorts, TikTok, Reels).
Repurposing a 9:16 phone recording for horizontal players (YouTube long-form, blog embeds).
Repurposing 4:3 archive footage for 3:4 mobile, or vice versa.

The output aspect is the source aspect with width and height swapped — 16:9 → 9:16, not "letterboxed 16:9 in a 9:16 frame".

When NOT to use

Multi-person Q&A where each face needs its own crop — this skill picks one crop track per video. For per-speaker split renders, use wjs-editing-multicam instead.
Animated content / B-roll with no faces — falls back to center crop, usually wrong for the intent.
Heavy camera motion in the source (handheld pan/zoom) — the face tracker amplifies camera shake. Stabilize first.
Source already at target aspect — no work to do.

What this skill IS — and IS NOT

Is	Is not
Visual active-speaker detection via MAR (mouth-aspect-ratio) variance	Audio-visual fusion (audio energy + lip motion cross-correlated)
Stable face tracking across frames by center-distance matching	Re-identification across long gaps / occlusions
Speaker-aligned segments with hysteresis to prevent flicker	Frame-by-frame switching on every flicker
`--face-pick speaker` (default) — pick whoever's mouth is moving	`--face-pick largest` (opt-in legacy) — pick largest face
Hard cuts between segments, fixed crop within each segment ( `--motion cut` , default)	Smooth panning that drifts during a speaker's turn (opt-in `--motion smooth` )
Audio stream-copy (bit-exact)	Audio reprocessing / re-encoding
MediaPipe Tasks `FaceLandmarker` (478-pt mesh) at 5 fps sampled via ffmpeg	Per-frame neural inpainting / out-painting
One `ffmpeg crop + scale` pass	Frame-by-frame Python compositor

Falls back to "largest face" automatically when no one is talking (silence, music-only stretches).

Dependencies

pip install mediapipe opencv-python numpy

(MediaPipe lives outside the standard Python distribution; ffmpeg and ffprobe must be on

PATH

First-run model download: MediaPipe 0.10+ uses the Tasks API, which needs a

face_landmarker.task

model file (~4 MB). On the first call,

crop.py

downloads it to

~/.claude/skills/wjs-reframing-video/models/

and caches it for subsequent runs. The script fails offline on first run.

Range limitation: The bundled landmarker is tuned for faces within ~2 m of the camera (selfie / podcast / interview distance). Wide event shots with small faces may not detect — sample a frame first to confirm.

Crop math

Source aspect =

W / H

. Target aspect =

H / W

(inverted). Compute crop window:

Source orientation Crop window

Horizontal (W > H) → Portrait

W_crop = H × H / W

H_crop = H

(narrow vertical band)

Portrait (W < H) → Horizontal

W_crop = W

H_crop = W × W / H

(narrow horizontal band)

For 1920×1080 → portrait,

W_crop = 608

H_crop = 1080

. Final scale to 1080×1920 (upscale ~1.78×). For 1080×1920 → landscape,

W_crop = 1080

H_crop = 608

. Final scale to 1920×1080.

Override the final size via

--output-size 1080x1920

if you want native crop dimensions instead of upscaling.

Pipeline

Probe input dimensions, fps, duration via ffprobe.
Decide orientation — auto from aspect (
```
--target portrait|landscape
```
to override).
Sample frames at
--sample-fps
(default 5; high enough to catch mouth motion — Nyquist for speech is ~10 Hz, we need at least 4–5 fps).
Detect face landmarks per sampled frame with MediaPipe Tasks
```
FaceLandmarker
```
(478 landmarks). For each detected face record: center, size proxy, MAR (mouth-aspect-ratio = inner-lip vertical distance / horizontal mouth-corner distance).
Track faces across frames by center-distance matching → each face gets a stable
```
face_id
```
.
Per-sample active speaker: for each face track, variance of MAR over a sliding window (
```
--mar-var-window-sec
```
, default 1 s). The face with the highest variance is "speaking". Below
```
--mar-var-threshold
```
, no one is speaking → fall back to largest face.
Hysteresis: a candidate switch only commits if the new speaker is stable for
```
--min-segment-sec
```
(default 1.5 s). Shorter flickers are squashed — prevents the crop from ping-ponging on a one-frame mis-detection.
Speaker-aligned segments → for each segment, mean (cx, cy) of that speaker's face over the segment becomes the crop center, fixed for the full duration of the segment.
Build a ffmpeg step-function expression (
```
--motion cut
```
, default) that holds each segment's crop position constant and jumps instantly at each segment boundary — the visual feel of a real cut between camera angles. (
```
--motion smooth
```
switches to piecewise-linear pan between segment midpoints; rarely the right call for talking-head content because the camera appears to drift mid-sentence.)
Render one ffmpeg pass —
```
crop=W:H:x='expr':y='expr', scale=OUT_W:OUT_H
```
. The crop filter evaluates
```
x
```
and
```
y
```
per frame natively. Audio stream-copied.

scripts/crop.py

is the implementation. Output side effects:

```
<input>.crop.json
```
— sidecar with the crop plan
```
<input>_cropped.mp4
```
— final cropped + scaled video

Sidecar schema (

<input>.crop.json

)

{
  "_about": "wjs-reframing-video crop plan for cam_a.MOV. Active-speaker detected via MAR variance.",
  "_help": {
    "source_size":     "[width, height] in pixels.",
    "target_size":     "[width, height] of the final rendered output.",
    "crop_window":     "[width, height] of the moving crop in source coords.",
    "chunks":          "Speaker-aligned segments: {t0, t1, cx, cy, speaker_id}.",
    "face_pick_mode":  "speaker = MAR-variance active-speaker; largest = old behavior.",
    "speaker_id":      "Stable face track id. null means no face / silence fallback."
  },
  "schema_version": 2,
  "source": "cam_a.MOV",
  "source_size": [1920, 1080],
  "target": "portrait",
  "target_size": [1080, 1920],
  "crop_window": [608, 1080],
  "face_pick_mode": "speaker",
  "sample_fps": 5.0,
  "mar_var_window_sec": 1.0,
  "mar_var_threshold": 1.5e-4,
  "min_segment_sec": 1.5,
  "chunks": [
    {"t0":  0.0, "t1":  4.2, "cx": 808, "cy": 540, "speaker_id": 0},
    {"t0":  4.2, "t1": 11.6, "cx": 1182, "cy": 540, "speaker_id": 1},
    {"t0": 11.6, "t1": 14.0, "cx": 808, "cy": 540, "speaker_id": 0}
  ],
  "face_sample_count": 1234,
  "track_count": 2
}

Performance

Detection is the slow step. On Apple Silicon at 2 fps sampling, expect ~10–20× realtime (a 30-min source detects in ~1–2 min). Bumping
```
--sample-fps
```
makes detection slower but tracking more responsive.
Render is fast — single ffmpeg pass with hardware encode (
```
hevc_videotoolbox
```
on macOS). Often <1× realtime for a 1080p source.
For very long sources (>200 chunks), the ffmpeg expression gets cumbersome; the script auto-downsamples chunk midpoints to keep the expression under ~200 control points.

Common pitfalls

Mouth gestures aren't speech — a yawn, laugh, eating, or sucking-in-air all raise MAR variance. The detector can briefly mistake these for talking. For high-stakes content, eyeball the speaker timeline in the sidecar (the script prints a
```
face#N: Xs on screen (Y%)
```
summary) and re-run with a different
```
--mar-var-threshold
```
if needed.
Side-profile or down-tilted faces — when a face is rotated >60° from camera, MediaPipe may fail to land mouth landmarks reliably, so MAR variance flatlines. The speaker fallback to "largest face" kicks in. If you have a long stretch of profile shots, consider
```
--face-pick largest
```
.
Two faces with overlapping speech (interruption / talking over) — both faces have MAR variance, only one wins. The losing face is treated as listener. For accurate per-speaker tracking under crosstalk, use wjs-editing-multicam with separate cams.
Long stretches of silence (B-roll, music) — falls back to largest face. If the largest face is wrong (e.g. a listener stays still while the speaker's mic feeds music), you'll see drift. Pre-segment around music-only sections.
Source has burned-in lower-thirds / subtitles — for H→V, the lower band gets cropped out; for V→H, it stays but gets stretched. Strip burn-ins before running.
Wide-angle / fish-eye lenses — landmarks miss faces near edges. Pre-correct distortion with
```
ffmpeg lenscorrection
```
first.
Upscaling artifacts —
```
608×1080 → 1080×1920
```
is a 1.78× upscale and visible on sharp text. Render at native crop dims (
```
--output-size 608x1080
```
) and let the platform upscale, if you have overlays you want to keep sharp.
Output bitrate > platform limit — default is
```
--bitrate 12M
```
. WeChat Channels (视频号) caps at 10 Mbps; pass
```
--bitrate 8M
```
for that target.

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