AI Virtual Try-On for Mobile: Clothing Overlay Technology

A user uploads their photo to an app — and the AI must overlay clothing so that it looks natural, considering pose, proportions, and lighting. This is a challenging computer vision task solved by a combination of body segmentation, pose estimation, and image synthesis. We have been implementing such solutions for mobile apps for over five years, using the MediaPipe, HR-VITON, and Metal stack. Our stack includes GAN-based image synthesis, volumetric rendering, and edge computing for on-device parsing.

Usually, a project starts with an audit of the clothing catalog and quality requirements. If maximum quality for social media is needed, we choose photo try-on with server-side GAN inference. If interactivity in the app is required, we develop real-time AR based on MediaPipe Pose. In both cases, we ensure compliance with App Store Review Guidelines and user data protection. Our experience: 5+ years in mobile development and computer vision, with over 50 completed projects for fashion brands and retailers.

In one project for a fashion brand, we implemented photo try-on with processing on an A10 GPU, allowing users to get results in 1.5 seconds. Return reduction amounted to 25%, saving up to $25,000 per month on a $100,000 turnover, and return logistics costs decreased by $1.2 per order. Average investment for such integration starts from $15,000, recouping within 3-6 months. Get a consultation to assess the effect for your business.

How does AI virtual try-on work?

We support two modes: photo try-on (user uploads a photo, result in a few seconds) and real-time AR (live camera stream with clothing overlay in real time). For photos we use heavy GAN models like HR-VITON, for AR we use a lightweight mesh approach based on MediaPipe Pose. The key phrase "virtual clothing try-on AI" applies to both modes. Our mobile try-on implementation focuses on high accuracy and low latency.

Technical stack: from parsing to synthesis

On-device: MediaPipe Pose for 33 keypoints, Self-Correction Human Parsing (SCHP) for body part segmentation (converted to Core ML/TFLite). The AI clothes on photo feature uses SCHP for precise segmentation. On iPhone 13, parsing takes 300–500 ms on a 512x512 image. According to MediaPipe Pose, keypoint accuracy exceeds 95%.

// MediaPipe Pose Landmarker
let options = PoseLandmarkerOptions()
options.baseOptions.modelAssetPath = Bundle.main.path(forResource: "pose_landmarker_full", ofType: "task")!
options.numPoses = 1
options.minPoseDetectionConfidence = 0.5
options.minPosePresenceConfidence = 0.5
options.minTrackingConfidence = 0.5

let poseLandmarker = try PoseLandmarker(options: options)

let mpImage = try MPImage(uiImage: sourcePhoto)
let result = try poseLandmarker.detect(image: mpImage)

// Android: human parsing via TFLite
val interpreter = Interpreter(
    FileUtil.loadMappedFile(context, "schp_parsing.tflite"),
    Interpreter.Options().apply { addDelegate(GpuDelegate()) }
)

val input = Array(1) { Array(512) { Array(512) { FloatArray(3) } } }
val output = Array(1) { Array(512) { Array(512) { FloatArray(20) } } }

interpreter.run(input, output)

Server-side try-on: HR-VITON — state-of-the-art model with resolution up to 1024×768. API on FastAPI + PyTorch:

@app.post("/tryon")
async def virtual_tryon(person_image: UploadFile, clothing_image: UploadFile):
    person = load_image(await person_image.read())
    clothing = load_image(await clothing_image.read())
    parse_map = run_human_parsing(person)
    keypoints = run_pose_estimation(person)
    result = hrviton_model(person, clothing, parse_map, keypoints)
    return StreamingResponse(image_to_bytes(result), media_type="image/jpeg")

Generation time on A10 GPU — 1.5–3 seconds. On CPU — 15–30 seconds.

Why is real-time AR more complex?

For real-time without heavy GAN we use mesh warping: MediaPipe Pose (30+ fps), Delaunay triangulation, texture deformation via Metal. Quality is lower, but it works on iPhone 11 without lag. Real-time AR is 30 times faster than photo try-on in response time, though it loses in detail. Performance comparison:

Content pipeline: each clothing item requires a photo on a white background, a silhouette mask, and a category. Auto-segmentation via RemBG, validation, upload to CDN. This clothing catalog preparation ensures consistent quality.

Implementation process: step by step

1. Catalog and requirements audit — analysis of assortment, formats, target devices.
2. Architecture selection — photo try-on, AR, or combination.
3. On-device ML integration — install MediaPipe, Core ML/TFLite, port SCHP.
4. Server inference setup — GPU inference (A10/A100) or dedicated server.
5. Try-on UI development — position selection, video capture, result display.
6. Testing and optimization — speed and quality measurements, stress testing.
7. Deployment — app store release, CDN setup for content.

What is included in the work

• Architecture and API documentation
• Access to the source code repository
• Integration with your catalog
• Test period and 3-month support
• Team training on pipeline operation
• Deployment scripts and CI/CD configuration

These deliverables ensure smooth integration and long-term success. The on-device ML components and server infrastructure are fully documented.

Timelines and investment

Photo try-on with server inference, one platform — 4–6 weeks. Full implementation with real-time AR, both platforms, and catalog pipeline — 10–16 weeks. Cost is calculated individually — typical investment starts from $15,000 for a basic photo try-on module. Return reduction averaging 15-25% recoups the investment in 3-6 months.

We work with retailers, fashion brands, and startups. We have completed over 50 projects in the fashion tech space, with 5+ years of experience. We guarantee quality at all stages. Contact us for a free audit of your catalog.

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.