Vox Director

Vox Director

Turn a one-line topic into a finished Vox-style paper-collage video: a bold, punchy, narrated explainer/ad where each beat is a torn-paper collage poster that comes alive, with voice-over, music and captions. Runs on one Atlas Cloud API key + local ffmpeg.

The look is the modern editorial paper-collage popularized by Vox explainers and creators like Stav Zilber / rom1trs: hand-cut paper cut-outs, torn edges, tape, halftone dots, newspaper clippings, bold flat color per beat, big cut-out headlines.

The core idea (read this first)

The Vox collage look and the collage motion are two different steps:

1. The look is born in the IMAGE step. Each beat is a finished collage poster made by a text-to-image model. All the collage DNA (torn paper, cut-outs, halftone, bold color, headline text) lives in that image. If the image isn't a rich collage, nothing downstream will save it.
2. The motion is added after. By default an AI video model animates the whole poster (the "living poster" path — simple, automated). For dramatic piece-by-piece assembly you cut the poster into parts and drive them with the local keyframe engine (advanced path).

Everything hinges on the prompts. Before writing any image or video prompt, read references/prompt-guide.md — it has the exact prompt structures that make the difference between "a real Vox collage" and "a moving PowerPoint".

Prerequisites (check, don't skip)

• echo "${ATLASCLOUD_API_KEY:+set}" — if empty, tell the user to set it (get one at https://www.atlascloud.ai/console/api-keys) and stop.
• command -v ffmpeg ffprobe — required for assembly (brew install ffmpeg on macOS).
• python3 -c "import PIL" — Pillow, for captions/watermark overlays.

Standard workflow (topic → film)

This is the default, most-automated path. Every stage is one script, all driven by a single beats.json per project under out/<project>/.

1. Topic → beat map. First read references/beat-layer.md (the story layer) and pick a narrative arc that fits the topic (timeline for history, pas/bab for ads, how_it_works for explainers, man_in_hole for transformations, …). Then write out/<project>/beats.json following that arc: beat-1 headline must be a ≤3s hook; beat count per duration (30s→6–8, 60s→10–12); split each beat into 2 shots (wide+detail) with per-shot camera_move VARIED across adjacent beats (never repeat; static on the payoff) and rich element_motion (see step 4). Each beat: narration, title_cn/title_en, scene, bg, feel, hook. This draft is the first mandatory approval gate — show the user the beat map before generating (the aspect-routing approximation in step 4 is the other one). Examples in examples/.

2. Pick the visual style (hybrid — do this BEFORE keyframes). Do not reuse one house style for every topic. Read references/prompt-guide.md (§5 theme presets); pick 3–4 theme presets (styles.THEME_PRESETS: american-retro, swiss-modern, punk-zine, soviet-constructivist, wpa-propaganda, 70s-groovy, chinese-ink, atomic-age, newsprint-editorial) that fit the topic's era/culture/tone — or compose a custom theme by mixing the prompt-guide dimensions (medium/era/palette/type/finish) when none fit. Match the topic, not the language (an English film on Chinese history should look Chinese). A theme bundles the whole LOOK layer (idiom+palette+type+finish+mood+motion). Run a bake-off and let the user pick by eye — AI proposes, the library is the quality floor, the human decides. Set the pick as "theme": python3 scripts/style_bakeoff.py out/<project> american-retro,swiss-modern,punk-zine,atomic-age Set the chosen name as "collage_style" in beats.json (keyframes.py reads it).

3. Keyframes (the collage look). python3 scripts/keyframes.py out/<project> Generates one collage poster per beat/shot with google/nano-banana-2/text-to-image, headline text baked in. Compose prompts with the 5-part structure in references/prompt-guide.md. Verify each poster looks like a real layered collage before animating — re-roll cheap ($0.08) here rather than paying to animate a weak image.

4. Motion. python3 scripts/clips.py out/<project> Animates each poster with google/gemini-omni-flash/image-to-video. Two independent axes (see references/beat-layer.md §3, tested on our stack): • camera_move — ONE move per shot. Safe/default: {static, push_in, pull_out, pan, tilt, parallax}. Bold/experimental {orbit, dolly_zoom, roll, whip} are available, not banned — they can warp the flat art, so pair with constraints: loose and re-roll. Any custom phrase also passes through. • element_motion — where the energy lives; AI writes it per beat to fit that scene (not a template). Make it RICH (several elements moving) — be bold. A hero element flying across the frame (paper bird/plane/coins) is a great occasional punch on a key beat, not every shot (a flyer in every frame reads as a formula). motion_style = amplitude calm | punchy | max (the theme sets a default). constraints = strict (default: defect guards on — flat-2D, one-way, no-morph; best for clean text-heavy explainers) or loose (let the model explore 3D/bold moves; re-roll the misses). Headline text is hard-protected only on shots that have a title (detail shots without a headline are free to go wild). For real people / brand logos, Omni & Seedance refuse — set "video_model": "kwaivgi/kling-video-o3-pro/image-to-video". Aspect routing (styles.resolve_video_aspect, second approval gate): clips.py resolves doc["aspect"] against the chosen video_model's own supported ratios — exact match wins; Omni is 16:9/9:16 only, Kling reference-to-video adds 1:1, Kling image-to-video/video-edit and Seedance just follow the input/ratio param. When there's no exact match it picks the nearest ratio but stops and asks you to confirm (set "aspect_approx_confirmed": true once you have) rather than silently reframing the film — every clip in one run shares the same resolved aspect so the finished film is never mixed.

5. Voice + music. python3 scripts/audio.py out/<project> One consistent narrator via xai/tts-v1 + instrumental BGM via minimax/music-2.6. Pick voice_id to fit the topic + language (don't just keep the default) — see references/voices.md for the full roster (5 multilingual + ~66 native voices by language, with gender). Default leo (male, documentary). To narrate in a REAL person's own voice (the presenter of a C-roll photo, a brand voice), set voice.clone_ref to a local audio sample — narration switches to seed-audio voice cloning with a pinned-speaker, studio-clean template that keeps timing beat-stable (see gotchas: never hand seed-audio bare narration without that pin).

6. Assemble. python3 scripts/assemble.py out/<project> ffmpeg: normalize + concat all shots, lay the single narration ducked under the music, burn captions timed per beat, add the watermark. Output out/<project>/final.mp4.

7. Verify. You can't read an mp4 directly — extract frames to jpg and look: ffmpeg -ss <t> -i final.mp4 -vf "scale=640:-1,format=yuvj420p" -frames:v 1 f.jpg

Cadence — how long shots should be

A common mistake is one long shot per beat. On a 9:16 / social piece especially, a static 10s shot reads as dead air. Aim for a cut every ~4–6 seconds:

• Shots run 3–6s; never let a single shot exceed ~7s — beyond that the AI motion has nowhere to go and it feels static.
• A beat's narration is ~8–10s, so give each beat 2 shots (a wide establishing shot with the headline + a detail cut-in without it). The narration plays continuously across both; the visual cuts mid-sentence. This is the single biggest rhythm win.
• So a ~60s film is typically ~6 beats × 2 shots × ~5s = 12 shots, not 6 × 10s.
• Reuse the wide keyframe as shot a; generate a tighter detail scene for shot b. keyframes.py skips any shot that already has a keyframe_url, so adding b shots and re-running only generates the new ones.

Add a shots array to each beat (see schema). Give each shot its own short scene and motion; set "title": true only on the wide shot so the headline shows once per beat.

A-roll mode (talking-head → collage)

The standard workflow above is B-roll: a topic becomes AI-generated collage posters that get animated. A-roll is the reverse case — the user already has a real recorded talking-head video (a presenter speaking to camera) and wants it itself turned into the collage look, keeping their actual performance (face, lip movement, gestures) intact. There is no poster to generate; the "keyframe" is the presenter's own footage. Use A-roll when the user gives you a video file of themselves/a presenter talking, not a topic to write from scratch.

1. Transcribe + auto-segment. python3 scripts/asr_beats.py <project_dir> <source.mp4> Runs xai/stt-v1 on the source's own audio and cuts it into beats at sentence-ending punctuation or natural pause gaps (never exceeding ~9.5s, under Omni/Kling video-edit's 10s per-call cap). Writes beats.json with each beat's start/end/text — this is the same mandatory approval gate as the B-roll beat map: review it, set "theme" (run style_bakeoff.py the same way — the presenter's segment works fine as the bake-off source), and optionally fill in a content_beats string per beat (a sticker/stamp idea to layer in) before generating anything.

2. Generate. python3 scripts/aroll_clips.py <project_dir> [only_ids] Cuts each beat's time range out of the source, uploads it, and re-styles it with a photographic paper-cutout sticker treatment on the presenter — her real likeness, lip movement, eye-line and gestures follow the source frame-for-frame; only the silhouette edge and the world around her are paper-collage. Default model is google/gemini-omni-flash/video-edit; any beat it rejects automatically retries on bytedance/seedance-2.0/reference-to-video (set via video_model/video_model_fallback in beats.json). Never ask the model to redraw or halftone-texture the face itself — that gets rejected regardless of how the prompt is worded (tried both a strong and a softened phrasing; both failed). Uses the same aspect-routing confirm gate as clips.py.

3. Assemble. python3 scripts/aroll_assemble.py <project_dir> Muxes each generated clip with the original beat segment's own audio (never whatever audio the video model produced) so lip-sync is guaranteed regardless of which model handled that beat, normalizes every beat to one canvas, and concats into final.mp4.