OpenMontage/skills/creative/video-understand-usage.md

Video Understanding Usage for OpenMontage

Sources: OpenMontage video_understand tool implementation, CLIP/BLIP2/LLaVA model documentation, OpenCV image quality metrics

Quick Reference Card

DEFAULT MODE:     describe — generates captions for frames
FOR REVIEW:       quality — assesses blur, brightness, contrast
FOR Q&A:          qa mode with a query — "Is the speaker visible?" "Is the text readable?"
DEFAULT MODEL:    clip (fastest, good for classification)
FOR DETAIL:       blip2 or llava (slower, richer descriptions)
MAX FRAMES:       5 default for video — sample strategically, not exhaustively

When to Use video_understand

• Visual QA during review — check rendered output quality before delivering
• Footage analysis — understand what's in user-provided footage before planning
• Highlight extraction — identify the most visually interesting frames
• Quality gating — programmatic check for blur, exposure, scene coherence
• Scene classification — categorize footage by content type
• Asset validation — verify generated images match the intended scene description

Mode Selection

Mode	What It Does	When to Use
describe	Generates a text description of the frame	Understanding footage content, logging
qa	Answers a specific question about the frame	Targeted checks ("Is text readable?", "Is face visible?")
quality	Measures blur, brightness, contrast numerically	Automated quality gating, comparing takes
classify	Categorizes the scene type	Sorting footage, pipeline routing

Quality Mode Metrics

Metric	What It Measures	Bad	Good
blur_score	Laplacian variance	Below 100 = blurry	Above 500 = sharp
brightness	Mean pixel value (0-255)	Below 50 = too dark, above 200 = overexposed	50-200
contrast	Pixel standard deviation	Below 30 = flat/washed out	Above 80 = good contrast

Model Selection

Model	Speed	Capabilities	Best For
clip	Fast	Classification, similarity matching	Quick scene categorization, batch processing
blip2	Medium	Detailed captions, visual QA	Understanding complex scenes, answering questions
llava	Slow	Most detailed understanding, reasoning	Deep analysis, subjective quality assessment

Model Selection Rules

• Use clip for batch operations and classification tasks
• Use blip2 for describe and qa modes when detail matters
• Use llava only when you need the most thorough understanding

Frame Selection for Video

• Default samples max_frames (5) evenly across the video
• Use frame_indices to target specific frames (e.g., check quality at specific timestamps)
• For quality review, sample the first frame, middle frame, and last frame minimum

Common Workflows

1. Pre-Edit Footage Review

video_understand (describe, 10 frames) → inform scene_plan

Analyze user-provided footage before planning cuts or edits. Use blip2 for detailed descriptions that inform the scene plan.

2. Post-Render Quality Gate

video_understand (quality) → pass/fail → re-render if needed

Run after composing the final video. Fail if any frame has blur_score < 100, brightness outside 50-200, or contrast < 30.

3. Highlight Selection

video_understand (describe, 20 frames) → rank by visual interest → select clips

Sample many frames, describe each, then select the most visually compelling segments for a montage or trailer.

4. Asset Validation

video_understand (qa, "Does this match: [scene description]?") → confirm or regenerate

After generating an image or video clip, verify it matches the intended scene description before proceeding.

5. Talking-Head Analysis

video_understand (qa, "Is the speaker's face clearly visible?") → face_enhance if needed

Check face visibility and framing before applying lip-sync or face restoration tools.

Quality Checklist

• Descriptions accurately match what's in the frame
• Quality scores correlate with visual inspection (manually spot-check)
• QA answers are consistent across similar frames
• Classification categories are stable across adjacent frames
• No false positives in quality gating (good frames passing, bad frames failing)

Applying to OpenMontage

When using the video_understand tool:

1. Use quality mode as a post-render gate in the compose stage — reject outputs below quality thresholds
2. Use describe mode to analyze user-provided footage at the start of the talking-head pipeline
3. For batch quality checks, use clip model (fastest) — switch to blip2 only for detailed review
4. Sample at least 3 frames for quality assessment — beginning, middle, end
5. Quality thresholds for passing: blur_score > 100, brightness 50-200, contrast > 30
6. Use qa mode to validate generated assets: "Does this image show [expected content]?"
7. In the review stage, combine video_understand quality data with the reviewer skill's rubric
8. Do NOT run video_understand on every frame of a long video — sample strategically