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Artificial Intelligence · Capstone
Virtual Clothes Try-On
Overlays garments on a live camera feed that follows your pose
5-6 weeks build
Overview
Stand in front of a webcam and pick a garment: MediaPipe tracks shoulders, hips and arms, and the garment image is warped to follow your pose in real time, with body segmentation handling occlusion by your arms. A second, offline mode uses a pretrained image-based try-on model on the public VITON-HD dataset for higher-quality stills, and the report compares the two approaches.
What makes it stand up
- 01Real-time pose tracking with MediaPipe landmarks
- 02Landmark-driven garment warping with thin-plate splines
- 03Body segmentation so arms can pass in front of clothes
- 04Offline image-based try-on mode using VITON-HD
- 05React catalogue for picking and switching garments
Module breakdown
- 01
Pose tracking
Extracts and smooths body landmarks from the webcam.
- 02
Garment prep
Removes backgrounds and marks garment anchor points.
- 03
Warping
Fits garments to landmarks and blends them onto the frame.
- 04
Offline try-on
Runs a pretrained try-on network on still photos.
- 05
Frontend
React catalogue with a live preview served by FastAPI.
After this, you will be able to
- Explain how pose landmarks drive the garment warp
- Defend the trade-off between real-time and offline quality
- Demo a live try-on and switch garments on request
Technology stack
- Python
- MediaPipe
- OpenCV
- PyTorch
- FastAPI
- React
- Level
- Capstone
- Domain
- Artificial Intelligence
- Build time
- 5-6 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support