Hi everyone,
I wanted to share a project I recently completed: a virtual try-on mobile app that allows users to see how clothing and accessories look on them in real time. I thought it could be useful for anyone exploring computer vision, deep learning, or mobile AI applications.
Tech Stack & Tools:
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Frontend: Flutter for cross-platform mobile development (iOS + Android)
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Backend: Python + FastAPI for serving deep learning models
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Deep Learning Models:
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Pose estimation with MediaPipe to detect user keypoints
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Segmentation networks (U-Net / DeepLabV3) for isolating the user body from the background
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Generative models (GAN-based overlay) for realistic clothing fitting and texture blending
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Optimization: TensorFlow Lite / PyTorch Mobile for on-device inference to ensure smooth real-time performance
Approach:
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Captured user body keypoints via camera input using a pose estimation model.
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Applied semantic segmentation to separate the user from the background, enabling accurate overlay of clothing items.
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Used a lightweight GAN-based model to warp and blend clothes naturally according to the user’s pose.
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Integrated filters and adjustments (size, rotation, scaling) to ensure the virtual try-on looked realistic.
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Optimized the inference pipeline for mobile devices so users could see results in near real-time without lag.
Final Result:
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Smooth real-time virtual try-on experience on both Android and iOS.
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Accurate alignment of clothing items to different body poses.
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Lightweight app (<50MB) running at ~15–20 FPS on mid-tier smartphones.
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Fully modular architecture: easy to swap models or add new items.
I’d love to hear feedback or suggestions on improving realism or performance further, especially from anyone experimenting with GANs or mobile CV.
If you’re curious, I can also share a high-level architecture diagram or discuss how I handled edge cases like multiple users, occlusions, or clothing with complex patterns.
Best, Steve.