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.