AI Automatic Video Montage for Mobile App

AI-Powered Automatic Video Montage for Mobile Apps

Automatic video montage is when a user uploads 20 random vacation clips, and the app automatically selects the best moments, cuts them to the rhythm of music, and produces a finished video. Technically, this combines multiple AI components: content analysis, beat detection, scene selection, and final assembly.

Video Content Analysis

Before assembling, we need to understand what's in the clips. For each segment, we run:

Frame Quality Detection: measure blurriness using Laplacian variance (cv2.Laplacian), exposure (average brightness), and face presence. Blurry and poorly lit frames are excluded.

Highlights Detection: sudden changes in dynamics (camera movement, action), faces with emotions, high contrast — these all increase a moment's "score".

# Backend: frame scoring
def score_frame(frame_bgr):
    gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY)

    # Sharpness
    sharpness = cv2.Laplacian(gray, cv2.CV_64F).var()

    # Brightness
    brightness = gray.mean()
    brightness_score = 1.0 - abs(brightness - 128) / 128  # 128 = optimal

    # Motion (difference from previous frame)
    # motion_score = ...

    total = (sharpness / 1000) * 0.4 + brightness_score * 0.3 + motion_score * 0.3
    return min(total, 1.0)

For deeper analysis, use CLIP (OpenAI) via API: frame embeddings allow filtering by semantic content ("frames with people", "sunsets", "food").

Beat Detection and Music Synchronization

Montage to rhythm is what separates good auto-video from poor. Use librosa on the backend:

import librosa
import numpy as np

def detect_beats(audio_path):
    y, sr = librosa.load(audio_path, sr=22050)
    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
    beat_times = librosa.frames_to_time(beat_frames, sr=sr)
    return beat_times.tolist()  # seconds of each beat

Beats are cut points. The assembly algorithm: for each interval between beats, select the highest-scoring fragment; fragment length equals the interval between beats.

Average pop track BPM is 120–140. Beat interval is 0.43–0.5 seconds. These are short cuts — dynamic, suitable for TikTok/Reels. For lyrical videos, take every 2nd or 4th beat — 1–2 seconds per frame.

Mobile Client Architecture

The mobile app handles:

1. Selecting clips from gallery (multiple simultaneously — PHPickerViewController on iOS 14+, PhotoPicker on Android API 33+)
2. Uploading to backend (multipart upload with progress)
3. Selecting music (from library or generation via AI)
4. Displaying assembly progress
5. Playback and saving results

// iOS: multi-file upload with progress
class VideoUploadService {
    func uploadClips(_ urls: [URL]) -> AsyncStream<UploadProgress> {
        AsyncStream { continuation in
            Task {
                for (index, url) in urls.enumerated() {
                    let data = try! Data(contentsOf: url)
                    try await uploadSingle(data: data, name: "clip_\(index).mp4")
                    continuation.yield(UploadProgress(completed: index + 1, total: urls.count))
                }
                continuation.finish()
            }
        }
    }
}

Upload large files via URLSession background upload (URLSessionConfiguration.background). Doesn't lose upload when app is minimized.

Backend: Assembly via FFmpeg

After analysis and fragment selection, backend builds an FFmpeg command:

# Concatenation via concat demuxer
ffmpeg -f concat -safe 0 -i playlist.txt \
  -i background_music.mp3 \
  -shortest \
  -c:v libx264 -crf 20 -preset fast \
  -c:a aac -b:a 192k \
  -vf "scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2" \
  output.mp4

playlist.txt contains timestamps for each clip:

file '/tmp/clip_3.mp4'
inpoint 12.4
outpoint 13.1
file '/tmp/clip_7.mp4'
inpoint 5.2
outpoint 5.7

Server processing time: 30–120 seconds depending on source volume and result length.

Montage Style Settings

Good UX provides users with several presets:

Settings are passed to backend with the assembly request.

Timelines

Basic auto-montage (clip upload, beat-sync, assembly) — 2–3 weeks. Full implementation with CLIP content analysis, montage styles, AI music, and on-device preview — 6–8 weeks. Cost calculated individually.