video_editing_poc/docs/cinematic-video-editing-ser...

40 KiB

Cinematic Video Editing Service Implementation Plan

Goal

Extend the video clipping service so it can turn a folder or set of existing clips into a new cinematic promotional video.

Primary use case:

  • input: about 35 Porsche clips
  • output: one cinematic MP4 video
  • style: appealing, exciting, premium automotive promo
  • additions: music, sound effects, voiceover praising the Porsche, transitions, timing, and color treatment

The expensive AI model should do high-value creative planning only. The service should do the heavy media work with deterministic tools such as FFmpeg. A lower-cost coding model should be able to implement this plan milestone by milestone.

Core Principle

Do not send full-resolution video through an AI model.

Use the service to extract compact metadata and preview assets:

  • durations
  • codecs
  • dimensions
  • frame thumbnails
  • contact sheets
  • low-resolution preview proxies
  • optional audio loudness data
  • optional scene or motion scores

Then use AI to generate:

  • creative direction
  • shot selection
  • edit decision list
  • voiceover script
  • music and sound effect cue list
  • final render instructions

The service renders the final video from the plan.

Non-Goals

  • Browser-based nonlinear editor UI.
  • Manual timeline editing interface.
  • Real-time collaborative editing.
  • Full professional color grading system.
  • Training custom AI models.
  • Passing raw full-resolution video to an LLM.

Proposed Workflow

Input clips
  -> analyze media with ffprobe and ffmpeg
  -> extract thumbnails and low-resolution proxies
  -> build compact clip manifest
  -> AI creates cinematic storyboard and edit decision list
  -> service validates the plan
  -> service generates or imports voiceover, music, and SFX assets
  -> service renders final MP4 with ffmpeg
  -> service stores render output and manifest

Local AI Director Workflow

The service should support a local workflow where the video editing service runs on the user's machine and an AI coding/chat agent such as Codex or Claude acts as the director.

This mode should not require the AI agent to directly process video files. The service prepares a project folder containing metadata, thumbnails, contact sheets, and a strict prompt. The AI agent reviews those artifacts, writes an edit-plan.json, and asks the service to render the result.

Recommended local folder flow:

input/editing/source/
  porsche-drive-001.mp4
  porsche-drive-002.mp4

service scheduler
  -> detects a new source folder or batch
  -> creates output/edit-projects/<project-id>/
  -> analyzes clips
  -> extracts thumbnails and contact sheets
  -> writes analysis.json
  -> writes ai-director-prompt.md
  -> marks project as WAITING_FOR_DIRECTOR

AI director
  -> reads ai-director-prompt.md
  -> reviews contact-sheets/ and thumbnails/
  -> selects the strongest shots
  -> writes edit-plan.json
  -> optionally writes voiceover-script.txt and audio shopping list

service renderer
  -> validates edit-plan.json
  -> renders final.mp4
  -> writes render-manifest.json

Suggested local command:

mvn spring-boot:run -Dspring-boot.run.profiles=cinematic-editing-local

Then place a folder of clips here:

input/editing/source/porsche-session-001/

The service should produce:

output/edit-projects/<project-id>/ai-director-prompt.md
output/edit-projects/<project-id>/analysis.json
output/edit-projects/<project-id>/contact-sheets/
output/edit-projects/<project-id>/thumbnails/

The AI director should only need those generated artifacts to make editing decisions. It should not need to inspect full-resolution clips unless the user explicitly asks for manual review of a specific moment.

AI Director Responsibilities

The AI director should:

  • judge clips from thumbnails, contact sheets, metadata, and optional proxies
  • identify the best hero shots, detail shots, motion shots, and ending shot
  • choose clip order and trim ranges
  • write a strict edit-plan.json
  • write or update voiceover lines
  • suggest music and SFX cues
  • keep the final timeline within the configured target duration

The AI director should not:

  • render video
  • run heavy FFmpeg commands unless troubleshooting
  • modify source clips
  • invent clip IDs or timestamps outside analysis.json
  • depend on private absolute paths in viewer-facing text

Service Responsibilities In AI Director Mode

The service should:

  • watch a configurable local source directory for new project folders
  • claim one project folder at a time
  • ignore folders ending in .tmp, .part, .download, or .processing
  • create deterministic project IDs from folder names with numeric suffixes on collision
  • extract enough thumbnails for visual judgment
  • create contact sheets that are easy for an AI vision model to inspect
  • write a director prompt with exact instructions and JSON schema
  • wait for edit-plan.json
  • validate the plan before rendering
  • render final output deterministically

This mode allows a strong AI model to do creative direction once, then a cheaper model or the service itself can execute the mechanical steps.

Output Targets

Default final render:

  • container: MP4
  • video codec: H.264
  • audio codec: AAC
  • resolution: same as source if all clips match, otherwise configurable default 1920x1080
  • frame rate: same as source if all clips match, otherwise configurable default 30
  • audio sample rate: 48000
  • max duration: configurable, default 60 seconds

For Porsche promo videos, default creative structure:

0-5s: hook, hero reveal, engine or impact sound
5-15s: exterior beauty shots and badge/details
15-30s: driving motion, acceleration, road energy
30-45s: interior, craftsmanship, handling, lifestyle shots
45-55s: strongest montage sequence
55-60s: final hero shot and short tagline

Data Model

Add package:

src/main/java/org/example/videoclips/editing

Suggested domain records:

record EditProject(
        String id,
        String name,
        EditProjectStatus status,
        Path inputDirectory,
        Path outputDirectory,
        Instant createdAt,
        Instant updatedAt
) {}

enum EditProjectStatus {
    CREATED,
    ANALYZING,
    ANALYZED,
    WAITING_FOR_DIRECTOR,
    PLANNING,
    PLANNED,
    RENDERING,
    RENDERED,
    FAILED
}

record ClipAnalysis(
        String clipId,
        Path sourcePath,
        double durationSeconds,
        String videoCodec,
        String audioCodec,
        int width,
        int height,
        double frameRate,
        List<Path> thumbnails,
        Path contactSheet,
        Path proxyPath,
        double motionScore,
        double brightnessScore,
        double sharpnessScore
) {}

record EditDecision(
        String clipId,
        double sourceStartSeconds,
        double sourceEndSeconds,
        double timelineStartSeconds,
        double timelineEndSeconds,
        String transitionIn,
        String transitionOut,
        double playbackSpeed,
        String visualTreatment,
        String reason
) {}

record AudioCue(
        String type,
        String assetKey,
        double timelineStartSeconds,
        double timelineEndSeconds,
        double gainDb,
        String notes
) {}

record VoiceoverLine(
        String text,
        double timelineStartSeconds,
        double timelineEndSeconds,
        String delivery
) {}

record EditPlan(
        String projectId,
        String style,
        double targetDurationSeconds,
        List<EditDecision> decisions,
        List<AudioCue> audioCues,
        List<VoiceoverLine> voiceover,
        String renderProfile,
        String summary
) {}

For the first implementation, these can be JSON files on disk. Do not start with JPA unless project persistence is required by the user later.

API Contract

Add REST endpoints under /v1/edit-projects.

Create Edit Project

POST /v1/edit-projects

Request:

{
  "name": "porsche-cinematic",
  "inputDirectory": "./output/clips/20240708_173213",
  "targetDurationSeconds": 60,
  "style": "cinematic-porsche-promo",
  "voiceoverEnabled": true,
  "musicEnabled": true,
  "soundEffectsEnabled": true
}

Response:

{
  "projectId": "edit_01JZABCDE",
  "status": "CREATED"
}

Validation:

  • name: required, filesystem-safe after normalization
  • inputDirectory: required and must exist
  • targetDurationSeconds: 15..600
  • style: required, initially allow cinematic-porsche-promo

Analyze Clips

POST /v1/edit-projects/{projectId}:analyze

Behavior:

  • scans project input directory
  • accepts valid video files only
  • runs ffprobe
  • extracts thumbnails
  • creates contact sheets
  • optionally creates low-resolution proxy files
  • writes analysis.json

Response:

{
  "projectId": "edit_01JZABCDE",
  "status": "ANALYZED",
  "clipCount": 35,
  "analysisPath": "./output/edit-projects/edit_01JZABCDE/analysis.json"
}

Generate Storyboard Prompt

POST /v1/edit-projects/{projectId}:storyboard-prompt

Behavior:

  • reads analysis.json
  • produces a compact AI prompt
  • includes clip summaries, thumbnail references, timing constraints, and output schema
  • writes storyboard-prompt.md

Response:

{
  "projectId": "edit_01JZABCDE",
  "promptPath": "./output/edit-projects/edit_01JZABCDE/storyboard-prompt.md"
}

Save Edit Plan

PUT /v1/edit-projects/{projectId}/plan

Request:

{
  "style": "cinematic-porsche-promo",
  "targetDurationSeconds": 60,
  "decisions": [],
  "audioCues": [],
  "voiceover": [],
  "renderProfile": "mp4-h264-aac-1080p",
  "summary": "Cinematic Porsche promo edit."
}

Behavior:

  • validates the AI-generated edit plan
  • ensures all clip IDs exist
  • ensures source time ranges are inside clip durations
  • ensures timeline ranges do not overlap incorrectly
  • writes edit-plan.json

Render Edit

POST /v1/edit-projects/{projectId}:render

Behavior:

  • reads edit-plan.json
  • renders the final video with FFmpeg
  • mixes music, SFX, and voiceover if available
  • writes final MP4 and render-manifest.json

Response:

{
  "projectId": "edit_01JZABCDE",
  "status": "RENDERED",
  "outputPath": "./output/edit-projects/edit_01JZABCDE/final.mp4",
  "durationSeconds": 60
}

Read Project

GET /v1/edit-projects/{projectId}

Return current status, paths, timestamps, and error details if failed.

File Layout

For local filesystem implementation:

output/edit-projects/<project-id>/
  project.json
  analysis.json
  storyboard-prompt.md
  edit-plan.json
  render-manifest.json
  final.mp4
  thumbnails/
    <clip-id>_0001.jpg
    <clip-id>_0002.jpg
  contact-sheets/
    <clip-id>.jpg
  proxies/
    <clip-id>.mp4
  audio/
    voiceover.wav
    music.wav
    sfx/
  inbox/
    edit-plan.json

Configuration

Add YAML config under video-clipping.editing.

video-clipping:
  editing:
    enabled: true
    project-directory: ./output/edit-projects
    ffmpeg-binary: ffmpeg
    ffprobe-binary: ffprobe
    thumbnail-count-per-clip: 5
    contact-sheet-columns: 5
    proxy-enabled: true
    proxy-width: 640
    target-duration-seconds: 60
    output-width: 1920
    output-height: 1080
    output-frame-rate: 30
    audio-sample-rate: 48000
    video-bitrate: 12000k
    audio-bitrate: 192k
    local-director:
      enabled: true
      source-directory: ./input/editing/source
      working-directory: ./input/editing/working
      processed-directory: ./input/editing/processed
      rejected-directory: ./input/editing/rejected
      poll-interval-ms: 5000
      director-prompt-file-name: ai-director-prompt.md
      expected-plan-file-name: edit-plan.json
      auto-render-when-plan-appears: false

Local director defaults:

  • auto-render-when-plan-appears=false is safer for early development because the user can inspect edit-plan.json before rendering.
  • When set to true, the service should render automatically after a valid plan appears in the project inbox/ folder.
  • The scheduler should process one project folder at a time, using the same repeat-prevention pattern as the input folder scheduler.

FFmpeg Commands

Probe Clip

ffprobe -v error \
  -show_entries format=duration \
  -show_entries stream=codec_type,codec_name,width,height,r_frame_rate \
  -of json \
  clip_00001.mp4

Extract Thumbnail

Use timestamps spread across clip duration.

ffmpeg -hide_banner -y \
  -ss 3.2 \
  -i clip_00001.mp4 \
  -frames:v 1 \
  -q:v 2 \
  thumbnails/clip_00001_0001.jpg

Create Contact Sheet

ffmpeg -hide_banner -y \
  -i clip_00001.mp4 \
  -vf "fps=1/2,scale=320:-1,tile=5x5" \
  -frames:v 1 \
  contact-sheets/clip_00001.jpg

Create Proxy

ffmpeg -hide_banner -y \
  -i clip_00001.mp4 \
  -vf "scale=640:-2" \
  -c:v libx264 -preset veryfast -crf 28 \
  -c:a aac -b:a 96k \
  proxies/clip_00001.mp4

Render Final Video

First implementation should use a concat-based render:

  1. Trim each selected segment into an intermediate file.
  2. Normalize resolution, frame rate, pixel format, and audio.
  3. Concatenate intermediates.
  4. Mix music, SFX, and voiceover.
  5. Write final MP4.

This is simpler and easier for a lower-cost coding model than building one huge filter_complex command.

Intermediate segment command:

ffmpeg -hide_banner -y \
  -ss <sourceStart> \
  -to <sourceEnd> \
  -i <sourceClip> \
  -vf "scale=1920:1080:force_original_aspect_ratio=decrease,pad=1920:1080:(ow-iw)/2:(oh-ih)/2,format=yuv420p" \
  -r 30 \
  -c:v libx264 -preset veryfast -crf 18 \
  -c:a aac -b:a 192k -ar 48000 \
  <work>/segment_0001.mp4

Concat command:

ffmpeg -hide_banner -y \
  -f concat \
  -safe 0 \
  -i concat.txt \
  -c copy \
  <work>/timeline-video.mp4

Final mix command:

ffmpeg -hide_banner -y \
  -i <work>/timeline-video.mp4 \
  -i audio/music.wav \
  -i audio/voiceover.wav \
  -filter_complex "[1:a]volume=0.25[music];[2:a]volume=1.0[voice];[0:a][music][voice]amix=inputs=3:duration=first:dropout_transition=2[a]" \
  -map 0:v -map "[a]" \
  -c:v copy \
  -c:a aac -b:a 192k \
  final.mp4

Add SFX later after the basic music and voiceover mix works.

AI Storyboard Prompt Requirements

The generated prompt should instruct the AI to output strict JSON only.

Required input context:

  • project style
  • target duration
  • clip IDs
  • duration per clip
  • dimensions and frame rate
  • extracted thumbnail paths or descriptions
  • any motion, brightness, and sharpness scores
  • known constraints

Required output schema:

{
  "style": "cinematic-porsche-promo",
  "targetDurationSeconds": 60,
  "summary": "string",
  "decisions": [
    {
      "clipId": "clip_00001",
      "sourceStartSeconds": 0.0,
      "sourceEndSeconds": 2.5,
      "timelineStartSeconds": 0.0,
      "timelineEndSeconds": 2.5,
      "transitionIn": "cut",
      "transitionOut": "crossfade",
      "playbackSpeed": 1.0,
      "visualTreatment": "high contrast warm cinematic grade",
      "reason": "hero front angle"
    }
  ],
  "voiceover": [
    {
      "text": "This Porsche turns precision into emotion.",
      "timelineStartSeconds": 3.0,
      "timelineEndSeconds": 6.0,
      "delivery": "confident cinematic narrator"
    }
  ],
  "audioCues": [
    {
      "type": "music",
      "assetKey": "cinematic-driving-bed",
      "timelineStartSeconds": 0.0,
      "timelineEndSeconds": 60.0,
      "gainDb": -12.0,
      "notes": "build energy toward final montage"
    }
  ]
}

Prompt rules:

  • Never reference clip paths directly in final viewer-facing text.
  • Keep total timeline duration within the requested target duration.
  • Use at least 12 clips if enough usable clips exist.
  • Avoid using any single source clip for more than 20% of the final runtime.
  • Prefer a strong first 5 seconds.
  • End with the strongest exterior or motion shot.
  • Voiceover should praise the car without sounding exaggerated or fake.

AI Director Prompt File

In local director mode, generate ai-director-prompt.md for Codex, Claude, or another AI instance.

The prompt must include:

  • project ID and style
  • target duration
  • path to analysis.json
  • paths to contact sheets and thumbnails
  • exact output location for the plan
  • strict JSON schema for edit-plan.json
  • validation rules
  • instruction to judge shots from thumbnails/contact sheets
  • instruction to produce a cinematic Porsche promo edit

Suggested prompt content:

You are the director for a cinematic Porsche promo edit.

Use only the project artifacts in this folder:

- `analysis.json`
- `contact-sheets/`
- `thumbnails/`
- optional `proxies/`

Your task:

1. Review the contact sheets and thumbnails.
2. Pick the strongest shots.
3. Create a cinematic edit plan with a strong opening, rising energy, and final hero shot.
4. Write voiceover lines that praise the Porsche in a premium but believable tone.
5. Add music and SFX cues.
6. Write strict JSON to `inbox/edit-plan.json`.

Do not render video. Do not modify source clips. Do not invent clip IDs.

The service should write a matching director-readme.md with short human instructions:

Open ai-director-prompt.md in Codex or Claude. Let the AI inspect the generated thumbnails and contact sheets. When it writes inbox/edit-plan.json, call the render endpoint or enable auto-render.

Local Director Scheduler

Add a scheduler similar to the input folder scheduler, but for edit project folders.

States:

source -> working -> processed
source -> working -> rejected

Folder example:

input/editing/source/porsche-session-001/
  clip_00000.mp4
  clip_00001.mp4

Claimed folder:

input/editing/working/porsche-session-001/

Project output:

output/edit-projects/porsche-session-001/

Scheduler behavior:

  1. scan local-director.source-directory
  2. pick the first valid project folder in deterministic order
  3. move it to working-directory
  4. create an edit project
  5. analyze clips
  6. generate ai-director-prompt.md
  7. write status WAITING_FOR_DIRECTOR
  8. move the source folder to processed-directory after project creation succeeds
  9. leave the project output folder ready for the AI director

Plan pickup behavior:

  1. scan project inbox/ folders for edit-plan.json
  2. validate the plan
  3. if auto-render-when-plan-appears=true, render automatically
  4. otherwise expose the project as PLANNED and wait for POST /v1/edit-projects/{projectId}:render

Tests:

  • ignores temporary project folders
  • processes one project folder at a time
  • creates project artifacts and director prompt
  • does not reprocess a processed folder
  • rejects empty folders
  • validates plan pickup
  • respects auto-render-when-plan-appears

Voiceover And Audio

First implementation can support imported audio assets only:

  • user places music.wav and voiceover.wav under project audio/
  • service mixes those files into the final render

Second implementation can add generated voiceover:

  • create voiceover-script.txt
  • call an external TTS provider or OpenAI audio model outside the core renderer
  • save voiceover.wav
  • keep generation behind an interface so it can be mocked in tests

Suggested interface:

interface VoiceoverGenerator {
    Path generateVoiceover(String projectId, List<VoiceoverLine> lines);
}

Suggested first adapter:

NoopVoiceoverGenerator

It writes the script only and requires an imported audio file.

Milestones

  1. Add editing configuration under video-clipping.editing.
  2. Add filesystem project store for project.json, analysis.json, edit-plan.json, and render output.
  3. Add edit project domain records and status enum.
  4. Add POST /v1/edit-projects and GET /v1/edit-projects/{projectId}.
  5. Add clip discovery for project input directories.
  6. Add ffprobe analysis and validation for all input clips.
  7. Add thumbnail extraction.
  8. Add contact sheet generation.
  9. Add optional proxy generation.
  10. Add analysis.json writing and reading.
  11. Add storyboard prompt generation from analysis.json.
  12. Add local AI director prompt generation with thumbnail/contact-sheet instructions.
  13. Add local director source-folder scheduler.
  14. Add project inbox/ plan pickup.
  15. Add strict JSON edit plan schema and validation.
  16. Add PUT /v1/edit-projects/{projectId}/plan.
  17. Add simple segment rendering without transitions.
  18. Add concat-based final timeline rendering.
  19. Add music and voiceover audio mixing.
  20. Add render manifest with command metadata and output details.
  21. Add basic transition support: cut, crossfade, fade in, fade out.
  22. Add SFX cue support.
  23. Add optional generated voiceover adapter interface.
  24. Add structured logs and metrics for analysis, planning, and rendering durations.
  25. Add integration test with generated fixture clips and a short edit plan.
  26. Add documentation for running a local cinematic edit with Codex or Claude as director.
  27. Run mvn verify and update this checklist.

Milestone Details

Milestone 1: Editing Configuration

Files:

  • src/main/java/org/example/videoclips/config/VideoClippingProperties.java
  • src/main/resources/application.yml

Add nested properties class:

private final Editing editing = new Editing();

public Editing getEditing() {
    return editing;
}

Add fields matching the YAML in this plan.

Tests:

  • add a focused config binding test or extend an existing Spring context test
  • verify defaults bind correctly

Acceptance criteria:

  • application starts with editing config present
  • editing can be disabled with video-clipping.editing.enabled=false

Milestone 2: Filesystem Project Store

Files:

  • src/main/java/org/example/videoclips/editing/EditProjectStore.java
  • src/main/java/org/example/videoclips/editing/FileSystemEditProjectStore.java

Responsibilities:

  • create project directories
  • read/write JSON files with Jackson
  • normalize project IDs and reject path traversal
  • expose methods for project, analysis, plan, and manifest paths

Tests:

  • creates expected folder structure
  • rejects invalid project IDs
  • round-trips project.json

Acceptance criteria:

  • no direct filesystem writes from controllers
  • all project path creation goes through the store

Milestone 3: Domain Records

Files:

  • src/main/java/org/example/videoclips/editing/EditProject.java
  • src/main/java/org/example/videoclips/editing/EditProjectStatus.java
  • src/main/java/org/example/videoclips/editing/ClipAnalysis.java
  • src/main/java/org/example/videoclips/editing/EditPlan.java
  • src/main/java/org/example/videoclips/editing/EditDecision.java
  • src/main/java/org/example/videoclips/editing/AudioCue.java
  • src/main/java/org/example/videoclips/editing/VoiceoverLine.java

Tests:

  • JSON serialization for representative records

Acceptance criteria:

  • records serialize with stable camelCase JSON
  • no business logic in records beyond validation helpers if needed

Milestone 4: Project API

Files:

  • src/main/java/org/example/videoclips/api/EditProjectController.java
  • src/main/java/org/example/videoclips/api/dto/CreateEditProjectRequest.java
  • src/main/java/org/example/videoclips/api/dto/EditProjectResponse.java

Tests:

  • create project success
  • reject missing input directory
  • reject invalid target duration
  • get project success
  • get missing project returns 404

Acceptance criteria:

  • API returns project ID and status
  • project state is persisted to project.json

Milestone 5-10: Analysis Pipeline

Files:

  • src/main/java/org/example/videoclips/editing/EditProjectAnalyzer.java
  • src/main/java/org/example/videoclips/editing/FfmpegClipInspector.java
  • src/main/java/org/example/videoclips/editing/ThumbnailExtractor.java
  • src/main/java/org/example/videoclips/editing/ContactSheetGenerator.java
  • src/main/java/org/example/videoclips/editing/ProxyGenerator.java

Implementation notes:

  • reuse patterns from FolderVideoValidator and FolderFfmpegClipper
  • keep process execution behind package-private interfaces for tests
  • sanitize process output before logging
  • process clips in deterministic filename order

Tests:

  • analysis ignores non-video files
  • invalid videos are reported in analysis errors
  • command construction is correct
  • output JSON includes all analyzed clips
  • proxy generation can be disabled

Acceptance criteria:

  • analysis.json is enough for an AI prompt without reading raw video
  • failure of one invalid clip does not fail the entire project if at least one valid clip exists

Milestone 11: Storyboard Prompt

Files:

  • src/main/java/org/example/videoclips/editing/StoryboardPromptGenerator.java

Tests:

  • prompt includes clip IDs, durations, target duration, style, and JSON schema
  • prompt excludes raw absolute system paths unless explicitly configured

Acceptance criteria:

  • generated prompt can be pasted into an AI model
  • model output requirements are strict JSON

Milestone 12: Local AI Director Prompt

Files:

  • src/main/java/org/example/videoclips/editing/AiDirectorPromptGenerator.java

Behavior:

  • generate ai-director-prompt.md
  • include project style, target duration, clip metadata, contact sheet paths, thumbnail paths, and strict output schema
  • write human-readable director-readme.md
  • instruct the AI to write inbox/edit-plan.json

Tests:

  • prompt contains analysis.json
  • prompt contains contact-sheets/ and thumbnails/
  • prompt contains exact output path inbox/edit-plan.json
  • prompt instructs the AI not to render video

Acceptance criteria:

  • a user can open the prompt in Codex or Claude and get a valid edit plan without additional explanation

Milestone 13: Local Director Source-Folder Scheduler

Files:

  • src/main/java/org/example/videoclips/editing/LocalDirectorScheduler.java
  • src/main/java/org/example/videoclips/editing/EditProjectDirectoryInitializer.java

Behavior:

  • create configured source, working, processed, rejected, and project output directories on startup
  • scan video-clipping.editing.local-director.source-directory
  • claim one project folder by moving it to working
  • ignore hidden and temporary folders
  • create an edit project
  • analyze the clips
  • generate ai-director-prompt.md
  • set project status to WAITING_FOR_DIRECTOR
  • move the claimed source folder to processed after successful analysis

Tests:

  • creates all local director directories
  • selects project folders in deterministic order
  • ignores .tmp, .part, .download, and .processing folders
  • processes one project folder per scan
  • writes project prompt and analysis files
  • rejects empty project folders

Acceptance criteria:

  • dropping a folder into input/editing/source produces a ready-to-direct project under output/edit-projects

Milestone 14: Plan Inbox Pickup

Files:

  • src/main/java/org/example/videoclips/editing/EditPlanInboxScanner.java

Behavior:

  • scan project inbox/ folders for edit-plan.json
  • ignore partially written files with .tmp, .part, or .download suffix
  • validate the plan
  • save normalized plan to project root
  • mark status PLANNED
  • render automatically only when auto-render-when-plan-appears=true

Tests:

  • ignores temporary plan files
  • rejects invalid plan files
  • accepts valid plan files
  • does not render when auto-render is disabled
  • invokes renderer when auto-render is enabled

Acceptance criteria:

  • Codex or Claude can write inbox/edit-plan.json and the service can pick it up without a manual API call

Milestone 15-16: Edit Plan Validation

Files:

  • src/main/java/org/example/videoclips/editing/EditPlanValidator.java
  • src/main/java/org/example/videoclips/api/dto/SaveEditPlanRequest.java

Validation rules:

  • all clip IDs exist in analysis.json
  • sourceStartSeconds >= 0
  • sourceEndSeconds > sourceStartSeconds
  • source range is inside clip duration
  • timeline ranges are non-negative
  • timeline ranges do not overlap unless the transition requires overlap
  • final duration is within target duration tolerance
  • playback speed is between 0.25 and 4.0
  • only supported transitions are accepted

Tests:

  • accepts valid plan
  • rejects unknown clip ID
  • rejects out-of-range source timestamps
  • rejects overlapping timeline decisions
  • rejects unsupported transition

Acceptance criteria:

  • renderer never receives an invalid plan

Milestone 17-20: Render Pipeline

Files:

  • src/main/java/org/example/videoclips/editing/EditRenderer.java
  • src/main/java/org/example/videoclips/editing/FfmpegEditRenderer.java
  • src/main/java/org/example/videoclips/editing/RenderManifest.java

Implementation sequence:

  1. create working directory
  2. render each selected decision into normalized segment MP4
  3. write concat.txt
  4. concatenate segments into timeline-video.mp4
  5. mix audio when audio files exist
  6. write final.mp4
  7. write render-manifest.json

Tests:

  • command construction for segment render
  • command construction for concat
  • command construction for audio mix
  • render fails if no decisions exist
  • manifest is written on success

Acceptance criteria:

  • final MP4 exists after render
  • render manifest includes input clip IDs, output path, duration, and command summaries

Milestone 21-22: Transitions And SFX

Start simple.

Supported transitions:

  • cut
  • fade-in
  • fade-out
  • crossfade

Supported SFX cues:

  • imported WAV files only
  • cue has asset key, start time, gain, and optional fade

Tests:

  • unsupported transition rejected
  • SFX file missing is a validation error
  • final mix includes SFX input when cue exists

Acceptance criteria:

  • basic cinematic polish is possible without needing a full timeline engine

Milestone 23: Voiceover Generation Hook

Files:

  • src/main/java/org/example/videoclips/editing/VoiceoverGenerator.java
  • src/main/java/org/example/videoclips/editing/NoopVoiceoverGenerator.java

Behavior:

  • first version writes voiceover-script.txt
  • does not call external APIs
  • later adapters can call TTS providers

Tests:

  • script file generated from voiceover lines
  • noop adapter does not fail when TTS is disabled

Acceptance criteria:

  • service supports the workflow without requiring paid audio generation

Milestone 24: Logs And Metrics

Logs:

  • event=edit_project_created
  • event=local_director_project_claimed
  • event=edit_analysis_started
  • event=edit_analysis_completed
  • event=ai_director_prompt_generated
  • event=storyboard_prompt_generated
  • event=edit_plan_inbox_detected
  • event=edit_plan_saved
  • event=edit_render_started
  • event=edit_render_completed
  • event=edit_render_failed

Metrics:

  • video.clipping.edit.projects.created
  • video.clipping.edit.analysis.duration
  • video.clipping.edit.render.duration
  • video.clipping.edit.render.failures

Tests:

  • metrics binder test if custom metrics are added
  • at minimum, unit tests should cover event-producing paths

Acceptance criteria:

  • an operator can see which stage failed and how long rendering took

Milestone 25: Integration Test

Create generated fixture clips with FFmpeg:

  • three 3-second clips
  • different colors or test patterns
  • simple audio tones

Test flow:

  1. place a folder of generated clips in local director source
  2. run the local director scheduler
  3. assert ai-director-prompt.md and analysis.json exist
  4. write a short valid inbox/edit-plan.json
  5. run the plan inbox scanner
  6. render final video
  7. probe final video
  8. assert duration is greater than zero

Skip test when ffmpeg or ffprobe is unavailable.

Acceptance criteria:

  • proves the end-to-end renderer works on a machine with FFmpeg installed

Lower-Cost Model Implementation Instructions

Follow these rules when assigning this plan to a cheaper coding model:

  • Implement exactly one milestone at a time.
  • Run the relevant tests after each milestone.
  • Update the checklist in this file after each completed milestone.
  • Commit after each milestone if the repository is in a clean, test-passing state.
  • Do not refactor unrelated packages.
  • Reuse existing patterns from the folder scheduler for process execution, logging, and tests.
  • Keep the first renderer deterministic and simple.
  • Do not add external AI, TTS, or music-provider calls until the filesystem workflow works.
  • For local director mode, implement file handoff first. Do not embed Codex, Claude, or another model inside the Spring service.

Suggested prompt for the lower-cost coding model:

You are implementing `docs/cinematic-video-editing-service-plan.md`.

Implement the next unchecked milestone only. Keep the change scoped. Use existing patterns from the folder scheduler package where possible. Add tests for the milestone. Run the relevant Maven test command. Update the milestone checkbox only when tests pass. Do not implement future milestones early.

Suggested prompt for Codex or Claude acting as the local AI director:

You are the AI director for this local edit project.

Read `ai-director-prompt.md`, `analysis.json`, and the generated contact sheets and thumbnails. Create a cinematic Porsche promo edit plan. Write strict JSON to `inbox/edit-plan.json`. Do not render video and do not modify the source clips.

Local Runbook

Use this runbook after the local AI director milestones are implemented.

Prerequisites

Install:

  • Java 21
  • Maven
  • FFmpeg
  • ffprobe
  • Codex, Claude, or another AI instance that can read local project files and images

Verify tools:

java -version
mvn -version
ffmpeg -version
ffprobe -version

Start The Service

Run the service with the local cinematic editing profile:

mvn spring-boot:run -Dspring-boot.run.profiles=cinematic-editing-local

Expected startup behavior:

  • creates input/editing/source
  • creates input/editing/working
  • creates input/editing/processed
  • creates input/editing/rejected
  • creates output/edit-projects
  • logs that local director mode is enabled

Add Source Clips

Create one project folder under the local director source directory:

input/editing/source/porsche-session-001/

Place the clips inside that folder:

input/editing/source/porsche-session-001/clip_00000.mp4
input/editing/source/porsche-session-001/clip_00001.mp4
input/editing/source/porsche-session-001/clip_00002.mp4

Recommended copy flow for large files:

  1. copy the folder as porsche-session-001.tmp
  2. wait until all files are fully copied
  3. rename it to porsche-session-001

The scheduler should ignore temporary folders ending in .tmp, .part, .download, or .processing.

Wait For Analysis

The service should claim the folder and move it through:

input/editing/source/porsche-session-001
input/editing/working/porsche-session-001
input/editing/processed/porsche-session-001

After analysis, find the project output:

output/edit-projects/porsche-session-001/

Expected files:

output/edit-projects/porsche-session-001/project.json
output/edit-projects/porsche-session-001/analysis.json
output/edit-projects/porsche-session-001/ai-director-prompt.md
output/edit-projects/porsche-session-001/director-readme.md
output/edit-projects/porsche-session-001/contact-sheets/
output/edit-projects/porsche-session-001/thumbnails/
output/edit-projects/porsche-session-001/inbox/

Expected status:

WAITING_FOR_DIRECTOR

Ask The AI Director To Edit

Open Codex, Claude, or the selected AI instance in the project output folder.

Use this prompt:

You are the AI director for this local edit project.

Read `ai-director-prompt.md`, `analysis.json`, and the generated contact sheets and thumbnails. Create a cinematic Porsche promo edit plan. Write strict JSON to `inbox/edit-plan.json`. Do not render video and do not modify the source clips.

The AI should inspect:

  • analysis.json
  • contact-sheets/*.jpg
  • thumbnails/*.jpg
  • optional proxies/*.mp4

The AI should write:

output/edit-projects/porsche-session-001/inbox/edit-plan.json

Validate And Render

Optional imported audio assets must be placed before rendering:

output/edit-projects/porsche-session-001/audio/music.wav
output/edit-projects/porsche-session-001/audio/voiceover.wav
output/edit-projects/porsche-session-001/audio/sfx/engine-rev.wav

SFX plan entries use the filename without .wav as assetKey. When an accepted plan contains voiceover lines, the default noop provider writes voiceover-script.txt; record or generate that script as audio/voiceover.wav before rendering if narration should be included in the final mix.

If auto-render-when-plan-appears=false, trigger rendering manually:

curl -X POST http://localhost:8080/v1/edit-projects/porsche-session-001:render

If auto-render-when-plan-appears=true, the service should render automatically after it detects and validates:

output/edit-projects/porsche-session-001/inbox/edit-plan.json

Expected final output:

output/edit-projects/porsche-session-001/final.mp4
output/edit-projects/porsche-session-001/render-manifest.json

Review Output

Check:

  • final video exists
  • duration is close to target duration
  • music and voiceover are present if configured
  • no black frames at the beginning or end
  • no invalid clip references in render-manifest.json

Optional probe:

ffprobe -v error \
  -show_entries format=duration \
  -show_entries stream=codec_type,codec_name,width,height,r_frame_rate \
  -of json \
  output/edit-projects/porsche-session-001/final.mp4

Troubleshooting

No project appears in output/edit-projects:

  • confirm the service is running with cinematic-editing-local
  • confirm the source folder is not still named .tmp, .part, .download, or .processing
  • confirm the folder contains at least one valid video file
  • check logs for event=local_director_project_claimed

Analysis fails:

  • run ffprobe manually against one source clip
  • check whether the input file is still being copied
  • move bad files out of the project folder and try again

AI cannot decide from thumbnails:

  • increase thumbnail-count-per-clip
  • enable proxies with proxy-enabled=true
  • regenerate analysis

edit-plan.json is rejected:

  • confirm all clipId values exist in analysis.json
  • confirm source timestamps are inside each clip duration
  • confirm timeline timestamps do not overlap except for supported transitions
  • confirm the JSON is strict JSON with no markdown wrapper

Render fails:

  • check render-manifest.json if it exists
  • check FFmpeg logs
  • confirm all referenced source clips are still available under the project
  • temporarily disable music, SFX, and voiceover to isolate video rendering

Final video has no audio:

  • confirm the source clips have audio or imported audio files exist
  • confirm music.wav and voiceover.wav are valid WAV files if used
  • check the audio mix command in the render manifest

Final video is too slow to render:

  • lower output resolution
  • disable proxies only after analysis is complete
  • keep the first renderer concat-based before adding expensive transitions

Cost Strategy

The service should reduce expensive AI usage by producing compact artifacts.

Recommended AI input:

  • analysis.json
  • contact sheet images
  • optional short low-resolution proxy snippets
  • target duration and style

Avoid AI input:

  • full-resolution video files
  • every frame from every clip
  • raw uncompressed audio

Expected token ranges:

  • metadata-only storyboard: 5k-15k
  • metadata plus contact sheets: 30k-100k
  • dense frame analysis: 150k-400k+

The target architecture should keep normal cinematic edit planning in the 30k-100k range before rendering.

Completion Criteria

The feature is complete when:

  • a user can create an edit project from an existing folder of clips
  • a user can drop a folder into input/editing/source and get a ready-to-direct project locally
  • ai-director-prompt.md tells Codex or Claude exactly how to judge thumbnails and write inbox/edit-plan.json
  • the service can pick up an AI-generated edit-plan.json from the project inbox
  • the service can analyze clips and generate analysis.json
  • the service can generate a storyboard prompt
  • a valid edit plan can be saved
  • the service can render one final MP4
  • music and voiceover can be mixed into the output
  • a realistic FFmpeg integration test passes
  • the plan checklist is fully checked