E-Commerce Virtual Try-On AI Development
Budget: ₹12,500 – ₹37,500 INR
AI/ML Developer for Virtual Try-On Model (Integration-Ready for E-Commerce Platform)
Job Description:
We are building an e-commerce platform and want to add a Virtual Try-On feature where customers can see how clothes from our catalog (provided by suppliers) will look on a model before purchase.
We already have our platform; we only need the AI model trained and ready for integration as a feature.
The system should:
Take clothing images from our catalog and allow users to virtually “wear” them.
Support default avatars/models (male/female, different body types) for customers who do not want to upload their photo.
Allow customer photo upload for a personalized try-on experience.
Produce realistic results (proper alignment, sizing, and texture mapping).
Be integration-ready via API or codebase so our development team can add it as a feature to our website.
Be scalable and optimized for fast inference (real-time or near real-time).
Responsibilities:
Train and fine-tune a Virtual Try-On model (e.g., CP-VTON, Outfit-VTON, ClothFlow, or similar).
Prepare and preprocess clothing catalog images (masks, edges) for the model.
Deliver integration-ready API or code for website use.
Provide documentation and deployment guidance.
Optional: Suggest ways to handle new product uploads automatically without retraining the model.
Skills Required:
Strong background in Computer Vision, Deep Learning, and GANs.
Proficient in PyTorch or TensorFlow.
Experience with Virtual Try-On architectures (CP-VTON, Outfit-VTON, ClothFlow, etc.).
Backend/API development (Flask, FastAPI, Django).
Cloud deployment experience (AWS, GCP, Azure) is a plus.
Familiarity with e-commerce product catalogs or supplier integrations is preferred.
Deliverables:
Fully trained Virtual Try-On model.
Integration-ready API or code for the website.
Documentation for usage and deployment.
Optional: Guidance for handling new clothing uploads automatically.
Suggested Milestones:
Dataset preparation & preprocessing of catalog images.
Model training & sample outputs on catalog images.
Integration-ready API/code delivery.
Optimization & deployment guidance.
Job Description:
We are building an e-commerce platform and want to add a Virtual Try-On feature where customers can see how clothes from our catalog (provided by suppliers) will look on a model before purchase.
We already have our platform; we only need the AI model trained and ready for integration as a feature.
The system should:
Take clothing images from our catalog and allow users to virtually “wear” them.
Support default avatars/models (male/female, different body types) for customers who do not want to upload their photo.
Allow customer photo upload for a personalized try-on experience.
Produce realistic results (proper alignment, sizing, and texture mapping).
Be integration-ready via API or codebase so our development team can add it as a feature to our website.
Be scalable and optimized for fast inference (real-time or near real-time).
Responsibilities:
Train and fine-tune a Virtual Try-On model (e.g., CP-VTON, Outfit-VTON, ClothFlow, or similar).
Prepare and preprocess clothing catalog images (masks, edges) for the model.
Deliver integration-ready API or code for website use.
Provide documentation and deployment guidance.
Optional: Suggest ways to handle new product uploads automatically without retraining the model.
Skills Required:
Strong background in Computer Vision, Deep Learning, and GANs.
Proficient in PyTorch or TensorFlow.
Experience with Virtual Try-On architectures (CP-VTON, Outfit-VTON, ClothFlow, etc.).
Backend/API development (Flask, FastAPI, Django).
Cloud deployment experience (AWS, GCP, Azure) is a plus.
Familiarity with e-commerce product catalogs or supplier integrations is preferred.
Deliverables:
Fully trained Virtual Try-On model.
Integration-ready API or code for the website.
Documentation for usage and deployment.
Optional: Guidance for handling new clothing uploads automatically.
Suggested Milestones:
Dataset preparation & preprocessing of catalog images.
Model training & sample outputs on catalog images.
Integration-ready API/code delivery.
Optimization & deployment guidance.
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