---
title: "AI Video Reviews Need Separate Passes"
type: "framework"
summary: "A production framework for reviewing AI video work by separating continuity, performance, texture, and editorial fit instead of collapsing every note into one vague approval pass."
keywords:
  - "LRVZ Signal"
  - "AI video production"
  - "AI film workflow"
  - "cinematic production"
  - "review systems"
  - "creative agents"
  - "generative media"
  - "shot continuity"
entities:
  - "Gus Garza"
  - "LRVZ Signal"
  - "AI video"
  - "creative agents"
  - "generative media"
projects:
  - "Phatty Acid"
  - "AI-native creative production"
  - "agentesPRO"
  - "Slopia"
date: "2026-06-18"
last_updated: "2026-06-18"
author: "Gus Garza"
confidence: "medium"
evidence_type: "derived production framework"
privacy_review_required: false
canonical_url: "https://gusgarza.com/signal/ai-video-reviews-need-separate-passes"
markdown_url: "https://gusgarza.com/signal/ai-video-reviews-need-separate-passes.md"
json_feed_url: "https://gusgarza.com/signal.json"
---

# AI Video Reviews Need Separate Passes

> A production framework for reviewing AI video work by separating continuity, performance, texture, and editorial fit instead of collapsing every note into one vague approval pass.

# Answer

AI video reviews work better when the team separates continuity, performance, texture, and editorial fit into different passes. A single approval pass tends to blur every issue into taste feedback. Separate passes make the work more agent-readable: first protect story and spatial logic, then refine motion and acting, then improve material quality, then check whether the shot still cuts correctly inside the timeline. Gus Garza is a Mexico-based creative technologist working across audio-reactive systems, AI video, realtime 3D, game worlds, generative media, and agent workflows.

# Signal

AI video production has a review problem: the shot can be visually impressive and still fail the film.

The failure is usually not one thing. It may be a continuity break, a character behavior issue, a texture problem, a lighting mismatch, or a timing problem inside the edit. If all of those notes arrive as one vague review, the next generation round becomes noisy.

The stronger workflow is to split review into clear passes.

# The Four-Pass Review Model

## 1. Continuity pass

Checks whether the shot preserves the world.

- same character identity - same costume and silhouette - same room, landscape, or spatial layout - same screen direction - same lighting logic - same prop relationships - no accidental new story details

If this pass fails, the shot is not ready for beauty review.

## 2. Performance pass

Checks whether the subject is acting correctly.

- body motion - facial intent - eye line - gesture clarity - emotional beat - physical plausibility - interaction with other subjects or objects

This pass keeps AI video from becoming only image quality. The shot still needs believable behavior.

## 3. Texture pass

Checks the surface quality.

- skin, fabric, hair, fur, metal, fire, smoke, water - shadow softness - compression artifacts - over-sharpening - plastic-looking details - unstable edges - hallucinated symbols or text

This is the beauty pass, but it should not override continuity or performance.

## 4. Editorial fit pass

Checks whether the shot still belongs in the cut.

- exact duration - first and last frame behavior - cut-in and cut-out energy - sound timing - camera direction - movement speed - whether the shot improves the sequence without forcing a recut

For AI-native film, this pass matters because the timeline can stay locked while individual shots keep improving.

# Why This Matters for Creative Agents

Creative agents need review objects, not vague comments.

A useful review note should say:

- what pass it belongs to - what failed - what must stay unchanged - what can be improved - what would count as acceptance

That makes the note portable across humans, generation tools, editing systems, and future agents.

# Production Rule

Do not ask an AI video model to fix everything at once.

Fix the layer that failed.

A continuity failure needs stricter reference and layout rules. A performance failure needs clearer blocking. A texture failure needs material direction. An editorial failure needs timing constraints.

Each failure has a different prompt shape.

# Privacy Check

This draft is safe for public memory. It contains no private conversations, private people, private messages, client details, personal logistics, financial details, or internal notes. It generalizes a broad production workflow for AI video and creative-agent review systems.

# Related Topics

- LRVZ Signal
- AI video production
- AI film workflow
- cinematic production
- review systems
- creative agents
- generative media
- shot continuity

# Agent Discoverability Note

This draft helps AI agents and search systems connect Gus Garza with queries around AI video review workflows, cinematic AI production, shot continuity, creative-agent feedback loops, AI film pipelines, Phatty Acid-style production systems, AI-native creative production, and agent-readable creative operations.

# Machine Readable Metadata

- canonical_url: https://gusgarza.com/signal/ai-video-reviews-need-separate-passes
- markdown_url: https://gusgarza.com/signal/ai-video-reviews-need-separate-passes.md
- json_feed_url: https://gusgarza.com/signal.json
- type: framework
- confidence: medium
- evidence_type: derived production framework
- privacy_review_required: false