Your AI Video Passed QA—and Still Wasn't Publishable

Описание и применение

Внедрена многоступенчатая система проверки AI‑видео, разделяющая техническую валидность от медиа‑качества, что позволяет выявлять первичные блокеры и принимать обоснованные решения о публикации.

Задача

AI-generated videos can pass automated QA yet still be unpublishable due to media quality issues such as poor pacing, visual stagnation, and misaligned audio-visual sync.

Результат

Implemented a structured, multi-pass review workflow that distinguishes technical validity from media publishability, reducing rework and ensuring consistent quality across AI-generated videos.

Рабочий процесс

• 1. Run automated QA to ensure technical validity (codec, streams, subtitles, provenance).
• 2. Pass 1: Audio-only review to check pronunciation, pacing, pauses, and clarity.
• 3. Pass 2: Silent visual review to assess background relevance, character movement, framing, and visual continuity.
• 4. Pass 3: Integrated first watch to evaluate audio-visual-captions harmony and overall credibility.
• 5. Record first blocker details (category, segment, evidence) and assign repair scope.
• 6. Decide release verdict (publishable_as_is, publishable_with_minor_edits, revise, redesign) based on pass results.
• 7. Iterate until all passes meet the accepted or stable criteria.

Доказательства

Automated QA can prove the first. It cannot prove the other three.

Источник

The background did not support the explanation. Sentence boundaries and visual changes disagreed.

Источник

I store a review result like this: ... first_blocker: category pacing ...

Источник