AI Video Editing in Mobile Apps: Feature Implementation

AI Video Editing in Mobile Apps: Feature Implementation

A client sends a 15-minute interview recording with thirty pauses and "ums". Manually cutting them out would cost an hour. Our AI pipeline does it in 30 seconds. We implemented auto-cutting, background replacement, color correction, and smart reframe for iOS and Android. This article covers the technical details.

What Problems AI Editing Solves

AI video editing is not magic but a set of specific algorithms: automatic removal of pauses and filler words, background replacement, color correction by reference, and smart cropping to vertical format. Each requires its own stack and architecture. Let's review the key ones.

How Auto-Cut of Pauses Works

The user records a spoken video. We transcribe the audio via Whisper API or Deepgram, getting a JSON with timestamps for each word. Then we find pauses longer than 0.5 sec and words from a stop-list ("um", "ah", "like"). We generate an FFmpeg cut-list and assemble the final video.

# Backend: генерация FFmpeg фильтра из транскрипции Whisper
def build_cut_filter(transcript_words, pause_threshold=0.5, filler_words=None):
    filler_words = filler_words or {"эм", "ну", "вот", "как бы", "типа"}
    segments_to_keep = []
    prev_end = 0.0

    for i, word in enumerate(transcript_words):
        gap = word["start"] - prev_end
        if gap > pause_threshold:
            pass
        if word["word"].lower().strip(".,!?") in filler_words:
            continue
        segments_to_keep.append((word["start"], word["end"]))
        prev_end = word["end"]

    filter_parts = "+".join(
        f"between(t,{s},{e})" for s, e in merge_segments(segments_to_keep, gap=0.05)
    )
    return f"select='{filter_parts}',setpts=N/FRAME_RATE/TB"

Whisper with word_timestamps=True gives accuracy of ±20 ms — enough for smooth cuts. On the mobile device, the video is uploaded to the server, a task runs, and the result is downloaded. Playback uses AVPlayer or ExoPlayer.

Why Background Replacement on Mobile Is Challenging

For a static camera we use MediaPipe Selfie Segmentation — it runs in real time (30 fps) on modern devices. For a moving camera with multiple people — server-side processing via SAM 2 (Segment Anything Model 2).

MediaPipe on Android:

val options = ImageSegmenterOptions.builder()
    .setBaseOptions(BaseOptions.builder().useGpu().build())
    .setOutputCategoryMask(false)
    .setOutputConfidenceMasks(true)
    .build()
val segmenter = ImageSegmenter.createFromOptions(context, options)

The result is a confidence mask from 0 to 1. Apply it to each frame via Metal (iOS) or Vulkan (Android). For 1080p 30fps, GPU is mandatory — CPU cannot handle it. Server-side processing via SAM 2 gives better quality but takes 2–5 minutes per minute of video even on an A100.

Smart Crop for Format (Auto Reframe)

Converting 16:9 to 9:16 with intelligent cropping is an object tracking task. On mobile:

• Face detection on every keyframe (every 0.5 sec) — VNDetectFaceRectanglesRequest on iOS
• Build a trajectory of subject movement
• Smooth panning with an ease function
• FFmpeg crop filter with dynamic parameters

# FFmpeg: кроп с движением (x меняется от 0 до 540 за 10 сек)
ffmpeg -i input.mp4 \
  -vf "crop=608:1080:'min(max(cx-304,0),672)':0" \
  -c:v libx264 output_9x16.mp4

cx is the x-coordinate of the subject from tracking data. On the server, a Python script generates the FFmpeg command, executes it, and returns the result.

AI Color Correction

Using a reference photo, we apply a CinematicLUT via Core Image on iOS (about 100 ms per frame). For a text description ("make it look like golden hour"), we call a server that generates a LUT via Stable Diffusion + ControlNet. The resulting .cube file is applied to the video via FFmpeg: -vf lut3d=lut_file.cube.

Mobile Editor: Architecture

The timeline editor is a nontrivial UI challenge. Minimal stack:

• iOS: AVMutableComposition for tracks, AVVideoComposition for effects, AVAssetExportSession for export
• Android: MediaCodec + MediaMuxer for low-level processing or Media3 Transformer (recommended)

Media3 Transformer allows applying cropping, speed changes, and color in one pass with GPU acceleration via OpenGL ES. This is simpler than working directly with MediaCodec.

Comparison of Approaches: On-device vs Server

Choice depends on the task: for mass features (auto-cut) the server works; for real-time editing — on-device.

What's Included in the Work to Build an AI Editor

We deliver a turnkey result:

• Research and selection of the optimal stack for your task
• Implementation of backend (Python/FastAPI, Whisper, FFmpeg) and mobile SDK (iOS/Android)
• Integration with the existing app (code, dependencies, documentation)
• Setup of StoreKit 2 / Billing 6 for subscriptions if needed
• Testing on 10+ real devices
• Publishing to App Store and Google Play (assistance with Review Guidelines)
• 3 months of stable operation guarantee after launch

Our experience: 5+ years in mobile development, 20+ projects with video and AI, certified Apple and Google specialists. We guarantee quality and adherence to deadlines.

Timelines and Cost

• Single feature (auto-cut or background replacement) — 1–2 weeks
• Full editor with multiple features, timeline, and export — 6–10 weeks
• Cost is calculated individually after requirements analysis. Contact us — we will evaluate your project in 2 days.

Order development of an AI video editor for your mobile app. Get a consultation on technical details and timelines.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.