generate-footage

name

generate-footage

description

Build videos from stills when there is nothing to cut — narration first (ElevenLabs v3 with audio tags), then still keyframes from ANY source (AI image tools, screenshots, photos, HyperFrames-rendered HTML), animated deterministically with the animate-stills recipe (HyperFrames camera moves + GSAP atmosphere). Use when the user wants an explainer, documentary-style video, or any video built from generated or collected visuals instead of recorded footage.

generate-footage

The plugin's other entry point. video-use edits footage you HAVE. This skill builds videos from footage you DON'T have yet: narration plus still keyframes, composed and animated by HyperFrames, rendered deterministically.

Pipeline (proven in production; see docs/rca/ for why the motion step is deterministic):

script (with v3 audio tags)
  → eleven_v3 narration            (scripts/tts_v3.py)
  → transcribe                     (npx hyperframes transcribe narration.mp3)
  → lock scene windows to sentence boundaries (word-level timestamps)
  → STILLS FROM ANYWHERE           (one keyframe per scene — see below)
  → animate-stills composition     (recipes/04-animate-stills.md, build_scenes.py)
  → lint → render → motion + audio QA (scripts/motion_check.py, contact sheet)

Order matters: voice FIRST, visuals second

Generate and transcribe the narration BEFORE building any composition or collecting keyframes. The spoken sentence boundaries are the scene windows. v3 reads are non-deterministic in pacing — a 120s estimate can come out 100s or 166s — so visuals built first always get re-timed.

Voiceover — ElevenLabs v3 with audio tags

scripts/tts_v3.py — stdlib-only, no pip deps. Key from $ELEVENLABS_API_KEY or the plugin's skills/video-use/.env.

python3 scripts/tts_v3.py --probe                      # verify eleven_v3 + list voices
python3 scripts/tts_v3.py --voice <id> --stability 0.0 --script SCRIPT.md --out narration.mp3

• Audio tags go in the script text: [excited], [whispers], [softly], [dramatic tone], [warmly]. v3 performs them; v2 ignores them.
• Stability: 0.0 = Creative (max expressiveness), 0.5 = Natural, 1.0 = Robust. A "flat" read is fixed by lowering stability and re-tagging the script, not by louder words.
• Delivery levers: CAPS on power words, ... for dramatic pauses, contrast (drop to [whispers] right before a big line).
• Audition 2-3 voices on the first sentence before committing. The first voice is never the pick.
• v3 may paraphrase slightly. Transcribe and READ the result; regenerate if a key line drifted.

Stills from anywhere

A keyframe is just a PNG. The pipeline does not care where it came from. One still per scene, named kf-NN.png, listed in the scene manifest (recipes/04-animate-stills.md documents the schema). Sources that work:

• AI image tools — any text-to-image tool you already have (Gemini, GPT image, Midjourney, local SD, or a hosted actor — see the optional section at the end). Generate from per-scene prompts with one locked style suffix.
• Screenshots and photos — product UI captures, archive material, scans, real photography. The Ken Burns treatment was invented for this.
• HyperFrames-rendered HTML — build a styled HTML frame and snapshot it; the engine produces its own stills.

Craft rules that hold regardless of source:

• Lock ONE style in DESIGN.md and apply it to every frame. Drift across frames reads as different shots once there's motion and grading.
• Verify the file type. Some tools serve JPEGs with .png names; check magic bytes before composing (file kf-01.png).
• QA every frame (read the image). One off-style frame is a regenerate, not a keep. Check for: text artifacts, watermarks, anachronisms, faces in close-up (drift risk), wrong palette.
• No named IP, no real living person's likeness in generated frames for anything posted publicly.

Animation — deterministic, in-repo

Stills become motion with the animate-stills recipe (recipes/04-animate-stills.md): a manifest-driven HyperFrames composition applying camera language (push-ins, drifts, holds) and GSAP atmosphere (mist, rain, light shifts, vignette) over each still, scene windows locked to the transcript. scripts/build_scenes.py generates the scene layers from the manifest; scripts/motion_check.py proves real motion via pixel-diff in the rendered output. Renders identically every run, costs nothing, works offline.

Requires Node >= 22 for the HyperFrames CLI.

Captions and assembly

• Captions burned in (muted autoplay on Reddit/X), phrase-level, timed from the transcript words.
• White flash (0.28s) at story turns; soft crossfades elsewhere; dark vignette grade unifies stills from mixed sources and makes captions read.
• Verify motion in the render: pixel-diff two frames 4s apart inside one scene (scripts/motion_check.py — mean gray diff > 3 = real motion).
• Audio QA: ffmpeg -af volumedetect — documentary speech sits near -26 dB mean.

Optional: hosted generative tools

These are NOT dependencies. They are one way among several to source stills or clips, and they can disappear without notice — plan accordingly.

• Apify text-to-image actors (e.g. akash9078/ai-image-generator, ~$0.01/image) worked well for batch keyframe generation. Watch for silent failures (ok:true with a null URL) and mislabeled file types.
• Hosted image-to-video actors are where this pipeline previously kept its motion step. On 2026-06-11 the actor danitn11/wan22-lightning-image-to-video (2 total users) died upstream mid-production and every run failed instantly; the full analysis is in docs/rca/rca-generate-footage-animation-2026-06-11.md. Generative image-to-video now lives in a separate companion repo with a pluggable BYO-key backend; this plugin's motion step is deterministic and local.