Background
Myntra, a major Indian fashion e-commerce player, introduced an innovative feature—Virtual Try-On—to bridge the gap between physical and online shopping. With rising cart abandonment rates and high return volumes, Myntra turned to AI and ML to deliver personalized, interactive, and confidence-boosting shopping experiences.
Objective
To showcase how Myntra integrates artificial intelligence and machine learning in creating an immersive try-on experience that:
- Increases buyer confidence.
- Reduces returns and exchanges.
- Boosts personalization and engagement.
Key Technologies Used
1. Computer Vision & Pose Estimation
- Tools like OpenPose identify human keypoints (shoulders, hips, elbows).
- Enables real-time garment alignment based on posture and body orientation.
- Facilitates natural movement emulation during virtual try-on.
2. Generative Adversarial Networks (GANs)
- GANs simulate real clothing textures and adapt them over the user’s image or avatar.
- Dynamic lighting adjustment and garment distortion for realism.
- Differentiates between body parts and clothing layers for precision rendering.
3. 3D Modeling & Avatar Creation
- Users can upload a selfie or input their body parameters.
- AI builds custom avatars for realistic outfit simulation.
- Combines real-time try-on with garment simulation physics.
4. Recommendation Systems
- Collaborative filtering + content-based filtering for outfit suggestions.
- Tailored to body type, size preferences, and style trends.
- Uses TensorFlow/Scikit-learn to learn from user behavior and feedback.
5. NLP-Powered Chatbots
- Integrated AI assistant helps with sizing, color availability, and styling tips.
- Trained on FAQs and purchase history to offer smart responses.
- Enhances accessibility and customer satisfaction.
Data Sources Utilized
- User-uploaded selfies and avatars
- Product catalog and inventory
- Order history and return logs
- Public annotated datasets (e.g., COCO) for pose detection training
Results & Impact
- ✅ 38% increase in engagement time for try-on enabled listings
- ✅ 22% reduction in return rates of fashion items
- ✅ 31% boost in conversion for users using Try-On feature
- ✅ Enhanced personalization & customer satisfaction through AI-powered suggestions
Learning Outcomes
- Real-time AI personalization can drive conversions in fashion e-commerce
- GANs and pose detection significantly elevate the try-on realism
- AI reduces logistical costs by minimizing returns and increasing buyer clarity
- Multi-modal AI (CV + NLP + recommender systems) delivers superior UX
Future Enhancements
- AR Integration for full-body real-time try-on using mobile camera
- LiDAR and Depth Sensors for highly accurate garment fitting
- AI Styling Assistant powered by LLMs to help create outfits
- Predictive Size Engines trained on user feedback and physique estimation