SYSTEMS · AI VIDEO

AI Cinematography & Editing System

An AI post-production system that learns a creator's editing style and turns raw fashion footage into a publish-ready 9:16 cut in minutes.

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The problem

Fashion and product creators spend 30 to 60 minutes hand-cutting every short video: matching cuts to the music, picking the best takes, and holding each shot the right length. That manual work does not scale, and it buries the creator's own editing style under repetitive timeline labor.

The approach

Rather than generating video from text or bolting an AI button onto a timeline, this system works as an intelligent post-production control layer. It learns a creator's editing fingerprint from 10 to 30 of their finished videos, then makes editorial decisions that match it. It scores every raw clip against the learned style, orders the sequence to the beat, and returns a publish-ready 9:16 cut in 2 to 5 minutes. Creators refine by locking clips or giving instructions like "make this tighter" instead of dragging frames.

System design

Language / runtimePython 3.10+ backend, React 18+ frontend
ML modelsCLIP zero-shot shot classification, XGBoost classifier + regressor, PyTorch
Media processingOpenCV optical flow and quality scoring, librosa beat detection, FFmpeg
Core serviceFastAPI edit-decision engine over a Timeline JSON source of truth
RenderingRemotion headless export to 9:16 MP4
Data storePostgreSQL, Redis, S3
DeployAWS EC2 GPU instances, RDS, ElastiCache

How it works

01

Learn the style

Analyzes 10 to 30 finished videos to extract shot-length distributions, angle transitions, pacing, and energy preferences into a per-creator style profile.

02

Analyze the clips

Classifies each raw shot with CLIP, scores motion with optical flow, and rates quality using blur, exposure, and shake detection.

03

Detect the beats

Uses librosa to pull beat timestamps and normalize music energy to a 0 to 1 scale for timeline alignment.

04

Decide the edit

XGBoost models predict which clips to keep and how long to hold each, then a greedy look-ahead optimizer orders the sequence against style and beat constraints.

05

Edit and lock

The React timeline shows AI reasoning per clip; creators reorder, trim, or lock clips, and regeneration respects the locked decisions.

06

Render out

Remotion exports the Timeline JSON to a headless 9:16 MP4 ready to publish.

What it does

Style learning

Builds a model from a creator's own finished videos rather than imposing generic editing rules.

Automatic edit generation

Turns raw footage into a beat-synced, publish-ready cut in 2 to 5 minutes.

Explainable decisions

Surfaces a confidence score and reasoning for every clip so creators understand each cut.

Lock intent

Preserves manual edits; the system learns from locked clips and respects them on future regenerations.

Conversational refinement

Make Tighter, Add Energy, and Slow Down controls, plus section-specific regeneration with custom instructions.

Continuous learning

Logs every session and retrains on a cycle, turning new edits into positive and negative training examples.

Stack

PythonFastAPIPyTorchXGBoostCLIPOpenCVlibrosaFFmpegReactZustandTailwind CSSRemotionPostgreSQLRedisAWS
2-5 minPer edit
SoloBuild
Python · ReactStack
2025Year