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cover: cover images for short and mid-length videos

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Use when: a video is cut and needs a cover / thumbnail that replaces a dead first frame and doubles as the platform thumbnail (小红书, Shorts, TikTok, Reels, B站, YouTube). Three patterns: A 4-frame diagonal collage of stills, B the speaker’s matted face over a 2×2 grid of slides, C premium split cover (retouched face photo left, dark panel right with quote / title / thumbnail / chips) for 4:3 and 16:9 posts. Inputs: the cut video (and/or a face photo, slide PNGs). Outputs: cover.png (1080×1920, A/B) or cover-4x3.jpg + cover-16x9.jpg (C), plus one file per platform on request (see Platforms). Feed the cover to workflows/polish (--cover) or python -m vstudio.export --cover.

Run from the project folder; $VSTUDIO = repo root. Working files go in work/.

Prerequisites

Section titled “Prerequisites”
  • ./install.sh done (fonts in the cache, selfie_segmenter + face_landmarker models).
  • Chrome / Chromium / Edge for HTML renders (found via $CHROME, PATH, or standard install paths; a broken binary is skipped) — or pip install playwright && playwright install chromium as fallback.
  • Optional, Pattern B only: torch if you want the RVM matting engine (GPL-3.0, downloaded at runtime; see below).

Pick a pattern

Section titled “Pick a pattern”
Pattern Use when Bias
A collage preview content variety; face not needed on the thumbnail info density
B face on quadrants personal brand; the face drives CTR personal connection
C split cover horizontal post (小红书 4:3 / YouTube 16:9), quote or “亲测” angle, one hero thumbnail premium, editorial

A new channel usually gets more from B (the face builds recognition); an established one can lean on A (content density). Not for: static image posts (use an image editor), or a long-form YouTube thumbnail that should follow YouTube’s own conventions (big face, 3–5 words); --platform youtube only resizes these layouts to 1280x720.

Pattern A — collage

Section titled “Pattern A — collage”
  1. Find 4 moments (1 hook, 2–3 content, optionally 1 face): python3 $VSTUDIO/workflows/cover/scripts/extract_frames.py sheet my-talk.mp4 work/cover_src --every 5 → work/cover_src/contact_sheet.jpg with timestamps (--save-frames also writes full-size cand_<t>.png). Talking-head source? extract_frames.py pick my-talk.mp4 work/cover_src --top 6 ranks frames by smile / eyes open / centred face (vstudio.cover.score_frames) → pick<N>_<t>.png + picks_sheet.jpg.
  2. Pre-crop the cells (crop boxes are fractions of the decoded frame; append :face to use the face box): python3 $VSTUDIO/workflows/cover/scripts/extract_frames.py collage my-talk.mp4 work/cover_src 2.5 37 76 80:face Defaults fit a “slide on top, face below” layout; change --slide-box/--face-box for other layouts.
  3. cp $VSTUDIO/workflows/cover/templates/cover_collage.template.html work/cover.html, edit the tag, badges, headline and pills.
  4. python3 $VSTUDIO/workflows/cover/scripts/render_cover.py work/cover.html -o work/cover.png (1080x1920; add --platform xiaohongshu --platform douyin ... for per-platform sizes, see Platforms)

Pattern B — face on quadrants

Section titled “Pattern B — face on quadrants”
  1. Pick a talking frame (mid-word, mouth slightly open, eyes on camera; idle frames look posed) from the contact sheet (or the pick ranking above).
  2. Crop the face region, stopping above any burned-in caption band: python3 $VSTUDIO/workflows/cover/scripts/extract_frames.py face my-talk.mp4 work/cover_src/face_src.png --t 70 --box 0,0.5,1,0.948
  3. Optional retouch: python3 -m vstudio.retouch work/cover_src/face_src.png work/cover_src/face_src.png --slim .05 --eye .04 (--preset none|natural|daily|glam for makeup, --faces all for group shots; see references/RETOUCH.md) (PYTHONPATH=$VSTUDIO/lib if vstudio isn’t pip-installed).
  4. Matte: python3 $VSTUDIO/workflows/cover/scripts/matte.py work/cover_src/face_src.png work/cover_face.png --trim --preview
    • default engine: MediaPipe selfie segmenter + guided-filter edge refinement (Apache-2.0, fast, CPU).
    • --engine rvm: Robust Video Matting, cleaner hair. GPL-3.0, not vendored: torch.hub downloads it at runtime under its own licence. Needs torch; uses MPS/CUDA when available; warm-up passes are built in (RVM is recurrent, a single cold pass gives a soft alpha).
    • Caption still visible at the bottom? --crop-bottom 0.1.
    • Check work/cover_face.check.png (alpha over a checkerboard) for halos.
  5. Copy 4 clean slides (from workflows/slides) to work/cover_src/bg_tl.png … bg_br.png.
  6. cp $VSTUDIO/workflows/cover/templates/cover_face_quadrants.template.html work/cover.html; set .face-wrap width/height to the same aspect as the matted PNG (matte.py prints it). Otherwise object-fit:contain shrinks the face and leaves empty space. Example: PNG 1080×770 (1.40) → 980×700.
  7. Render as in A (render_cover.py).

Pattern C — premium split cover (retouched face left, dark panel right)

Section titled “Pattern C — premium split cover (retouched face left, dark panel right)”
  1. Grab the best face frame at full resolution (any aspect; it is scaled to the cover height) and retouch it: python3 -m vstudio.retouch work/cover_src/face.png work/cover_src/face_retouched.png --slim .05 --eye .04 --makeup .5 --preset natural (or put "retouch": {...} in the config and split_cover.py does it, cached as *.retouched.png).
  2. Optional hero thumbnail: a frame of the thing the video shows (an explainer frame, a product shot).
  3. Write work/split_cover.json from $VSTUDIO/workflows/cover/examples/split_cover.example.json:
    • quote (serif italic, highlight colour) + by line — a short third-party line that frames the post.
    • title.lines (CJK bold, auto-shrinks to the panel) with title.highlight substrings in brand.highlight.
    • thumbnail.path + crop fractions (rounded corners, drop shadow, hairline outline).
    • chips (outlined pills, ink|dim|highlight|accent), stamp (rotated brand.accent tag over the photo, e.g. 亲测), corner_tag (bottom-right brand.highlight tag, e.g. 记笔记 ↓).
    • outputs: one entry per size, e.g. 1440×1080 with photo_w 760 (4:3) and 1920×1080 with photo_w 900 (16:9).
    • face_x: "auto" centres the photo crop on the detected face (vstudio.face); a number (0–1) overrides.
  4. python3 $VSTUDIO/workflows/cover/scripts/split_cover.py work/split_cover.json [--platforms xiaohongshu:horizontal,xiaohongshu,douyin,youtube]
  5. Look at both sizes. 小红书 shows a centre crop of horizontal covers in the feed, so keep the face and the first title line inside the middle ~75 %. Title length rules: persona.platforms.<platform>.title_max.

Platforms

Section titled “Platforms”

Cover sizes come from vstudio.platform.cover_size (PLATFORMS.md):

platform cover feed shows
小红书 (xiaohongshu, 3:4 / 9:16 posts) 1080x1440 whole cover
小红书 horizontal (xiaohongshu:horizontal) 1920x1080 centre 4:3 (x 240..1680)
抖音 / TikTok (douyin, tiktok) 1080x1920 profile grid: centre 3:4
YouTube Shorts 1080x1920 whole cover
YouTube 1280x720, ≤ 2 MB; keep titles left of x 1100 (timestamp) whole cover
B站 1146x717 (16:10) whole cover
  • A / B (templates): render_cover.py page.html -o cover.png --platform xiaohongshu --platform douyin --platform youtube lays the page out at a design size of the same aspect (short side 1080), scales type with --fs, then resizes to the exact cover size; <out>.<platform>-<orientation>.png each, + .feed.jpg where the feed crops. Platform files are always suffixed, even for a single --platform (also douyin, which is 1080x1920 too), so they never overwrite the plain -o cover; render once without --platform if you need the unsuffixed cover.png.
  • C (split cover): an output with "platform": "..." takes its size from the profile. Portrait sizes (3:4, 9:16) use the stacked layout (photo band on top, panel below). For 小红书 16:9 the design is laid out feed-safe: the whole 4:3 design sits in the centre crop and the photo runs on to the left edge ("feed_safe": false turns it off); every platform output with a feed crop also writes <path>.feed.jpg. --platforms a,b,c adds outputs.
  • V-track talking-head covers: workflows/talkinghead/scripts/vertical/cover.py config.py --platform ... (same sizes).

Rules (all patterns)

Section titled “Rules (all patterns)”
  • Exact canvas sizes: 1080×1920 for A/B by default (per platform with render_cover.py --platform); whatever outputs says for C.
  • Accent. The collage / face-quadrant templates use --cover-accent = persona cover.accent (default teal #2dd4bf, their original look; render_cover.py --accent '#ff2442' for one render). They no longer follow brand.accent, which stays the on-video red. Pattern C (split cover) still uses brand.accent / brand.highlight (stamp, chips, title highlight) so it matches the video’s graphics. Plain python -m vstudio.render on a template renders teal at 1080x1920.
  • Headline pattern for A/B: Subject <span class="punch">is/isn't [contrarian punch].</span> (accent italic punch). It should be the same sentence as the script hook (see workflows/preproduction).
  • Fonts: the templates use @font-face on assets/fonts/* which vstudio.render copies from the repo font cache. Never point at system fonts.
  • Each quadrant / cell must show distinct content; four near-identical frames read as a glitch.
  • Trust the decoded frame size over ffprobe metadata (it has reported 720×1280 for a 1080×1920 file).
  • The cover only replaces the first ~1 s of picture (workflows/polish --cover); audio is untouched.

Self-check

Section titled “Self-check”
  • Canvas size exact; PNG opens with no broken transparency
  • Accent/highlight colours match the slides and on-video graphics
  • B: matte has no halo and no caption strip; .face-wrap aspect = PNG aspect
  • C: face and title line 1 survive a centre crop; quote fits on one line; chips don’t hit the right edge
  • Top tag / headline don’t cover the focal content
  • Headline = the script’s hook sentence, punch in accent italic (A/B)
  • File size sane (a 1080×1920 PNG lands around 0.5–1 MB; YouTube needs ≤ 2 MB)
  • After upload: the platform actually shows the custom cover (some swap back to a video frame)