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OpenMontage/skills/creative/broll-planning.md
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calesthio fdd6457fed docs(prompting): adopt 5-aspect video specification across skills
Incorporate the structured taxonomy from Lin et al. "Building a Precise
Video Language with Human-AI Oversight" (CMU/Harvard, arXiv 2604.21718v2).
The paper proves prompts structured around five aspects (Subject /
Subject Motion / Scene / Spatial Framing / Camera) unlock controllable
cinematography in fine-tuned video generation models. Off-the-shelf VLMs
already nail subject and scene; the gains live in motion, spatial, and
camera, which prompts routinely omit.

Universal layer (skills/creative/video-gen-prompting.md, +125 lines):
- 5-aspect prompt skeleton replaces flat formula
- Camera movements regrouped (translation / rotation / lens-only) with
  dolly!=zoom, pan!=truck, bird's-eye!=aerial disambiguations
- New primitive tables: camera height, camera angle, POV, lens
  distortion (fisheye vs barrel), focus / DoF (rack / pull / tracking),
  playback speed (6 modes), subject transitions
- Order-matters and self-contained-prompt rules
- Identity anchoring rule for multi-shot
- Strict static-shot rule, anti-subjective callout, overlays-not-depth
- Per-model word-count guidance

Per-model guides (sora, veo, hunyuan, ltx, seedance):
- Add the primitives each model honors literally
- Word-count sweet spots per model
- Strengthen seedance verbatim-identity and subject-transition guidance

Pipeline directors (cinematic / explainer / animation scene-director,
cinematic / explainer asset-director):
- 5-aspect scene-plan checklist (per-pipeline adapted)
- Overlays-not-depth callout
- Pre / critique / post self-review loop for generation prompts

Reviewer (skills/meta/reviewer.md):
- CHAI critique-quality rules: accurate / complete / constructive
- Critical findings now require a proposed_fix

Storytelling, cinematic, broll, video-reference-analyst:
- Anti-subjective rule (replace mood adjectives with visual causes)
- Camera-intent-per-beat for script writers
- POV column in stock-footage query templates
- 5-aspect structured output mandatory for reference-video analysis

skills/INDEX.md: video-gen-prompting marked as canonical 5-aspect spec.
2026-04-28 08:11:31 -07:00

7.2 KiB

B-Roll Planning for OpenMontage

How to plan B-roll needs from a script, decide between stock and generated footage, construct effective search queries, and evaluate footage quality.

When to Use

You are planning visual assets for a video and need supplementary footage (B-roll) to accompany narration, establish context, or add visual variety. This skill teaches you when to reach for stock footage vs. AI generation, and how to get good results from each.

The Decision Matrix: Stock vs. Generated

Scene Need Prefer Stock Prefer Generated
Real-world establishing shot (city, office, nature) Yes — stock excels here Only if no good stock match
People in realistic settings Yes — generated humans often look uncanny Only with high-quality models
Abstract concept visualization No Yes — AI can create what doesn't exist
Custom diagrams/infographics No Yes — use diagram_gen or image_selector
Branded/stylized imagery No Yes — AI matches your playbook style
Historical/archival footage Yes — stock libraries have archives No
Specific technical equipment Yes — real photos are more credible Only if equipment doesn't exist
Motion/action clips (waves, traffic, clouds) Yes — stock video is perfect for this AI video is catching up
Metaphorical imagery (growth, connection) Either works Yes — more creative control

Rule of thumb: If the scene needs to look real, use stock. If it needs to look specific to your concept, generate it.

Extracting B-Roll Needs from a Script

Walk the script section by section. For each section, ask:

  1. What is the narrator talking about? — The subject suggests the visual.
  2. Is there an enhancement cue? — The script writer may have embedded [B-ROLL: ...] cues.
  3. Does this section reference something concrete? — "servers in a data center" → stock footage of servers.
  4. Does this section explain an abstract concept? — "the algorithm weighs each factor" → generated diagram.
  5. How long is this section? — Determines clip duration needed.

Output: B-Roll Brief

For each identified need, create an entry:

Scene: s3 (15s-22s)
Need: Establishing shot of a modern data center
Source: stock
Keywords: ["data center", "server room", "rack servers blue light"]
Duration: 4-6 seconds
Orientation: landscape
Mood: cool, technological, clean
Fallback: AI-generated image of server racks

Constructing Effective Stock Search Queries

Query Construction Rules

  1. Be specific but not too specific. "aerial city skyline sunset" works. "aerial shot of downtown San Francisco financial district at 6:47pm golden hour" returns nothing.

  2. Use 2-4 keywords. Stock search is keyword-based, not semantic. More words = fewer results.

  3. Lead with the subject. "ocean waves" not "beautiful calm serene ocean waves at dawn."

  4. Include the visual quality you need:

    • Add "aerial" or "drone" for overhead shots
    • Add "close-up" or "macro" for detail shots
    • Add "timelapse" for time-lapse footage
    • Add "slow motion" for slow-mo clips
  5. Try synonyms on failure. If "programmer coding" returns poor results, try "developer laptop" or "software engineer workspace."

Query Templates by Scene Type

Add a POV keyword to every query. Stock libraries (Pexels, Pixabay, Storyblocks, Artgrid) explicitly index POV terms — drone, aerial, OTS (over-the-shoulder), macro, top-down, dashcam, FPV, handheld, locked-off — and adding the POV often unlocks better matches than refining the subject. The CMU/Harvard CHAI taxonomy treats POV as a first-class Scene aspect for the same reason: it changes which library shelf you're searching.

Scene Type Query Template Example with POV
Establishing [place] [time of day] [POV] "tokyo skyline night drone"
Activity [person] [action] [POV] "scientist microscope OTS"
Object [object] [style] [POV] "circuit board macro top-down"
Nature [element] [quality] [POV] "ocean waves aerial drone"
Abstract motion [movement] [style] [POV] "light trails timelapse locked-off"
Workplace [setting] [activity] [POV] "modern office meeting handheld"

If the scene description doesn't already imply a POV, ask the script/scene director — don't default to "no POV." A wrong-POV match (handheld when the scene needs drone) is harder to fix than a wrong color grade.

Evaluating Stock Footage Quality

When the stock tool returns results, evaluate before using:

Image Criteria

  • Resolution: Meets target (1080p minimum for video frames)
  • Relevance: Actually depicts what the scene needs (not just keyword match)
  • Style compatibility: Doesn't clash with the playbook's visual style
  • No watermarks: Pexels/Pixabay are license-free, but verify
  • Composition: Subject is well-framed, not cut off awkwardly
  • POV match: Does the footage's actual POV (drone, OTS, macro, handheld, locked-off, etc.) match what the scene needs? A wrong POV — e.g., handheld when the scene wants drone — is more costly to fix than a wrong color grade. Reject and re-query rather than try to crop your way out of it.

Video Criteria (all image criteria plus)

  • Duration: At least as long as the scene needs (can trim, can't extend)
  • Motion: Smooth, no jarring camera movement (unless that's the intent)
  • Frame rate: Matches target output (24/30fps standard)
  • Audio: Stock video audio is usually discarded — don't factor it in

Scoring Heuristic

Rate each result 1-5:

  • 5: Perfect match, use immediately
  • 4: Good match, minor crop or trim needed
  • 3: Acceptable, would benefit from color grading to match playbook
  • 2: Marginal — try different keywords first
  • 1: Wrong — doesn't match the scene at all

Threshold: Use results scoring 3+. Below 3, refine the query or switch to generated.

Failure Escalation

When stock search fails (no results or all score below 3):

  1. Retry with different keywords — try synonyms, broader terms, or different angles
  2. Try the other stock provider — Pexels and Pixabay have different libraries
  3. Switch to AI generation — use flux_image or openai_image with the scene description
  4. Escalate to user — "I couldn't find good stock footage for [scene]. Here are the best options: [show results]. Or I can generate an image instead. What do you prefer?"

The agent should only ask the user when both stock search AND generation fallback would produce suboptimal results. For most cases, the fallback chain handles it silently.

Attribution Tracking

Both Pexels and Pixabay are free for commercial use with no required attribution. However, best practice is to track sources in the asset manifest:

{
  "id": "broll-scene-3",
  "type": "image",
  "source_tool": "pexels_image",
  "provider": "pexels",
  "attribution": {
    "photographer": "Joey Farina",
    "source_url": "https://www.pexels.com/photo/...",
    "license": "Pexels License"
  }
}

This data is available in the tool's response (photographer, pexels_url / page_url). Include it in the asset manifest for transparency.