Week6

Week #6 #

Final deliverables #

Project Overview #

Looki is an innovative virtual fitting room application that revolutionizes online shopping by:

  • Creating accurate 3D body models from user photos
  • Providing precise size recommendations across brands
  • Enabling realistic virtual try-on experiences
  • Reducing clothing return rates by up to 40%

Key implemented features:

  • AI-powered body measurement extraction
  • Personalized 3D avatar generation
  • Size recommendation system
  • Virtual clothing try-on functionality
  • Cross-platform mobile experience

Implemented Features #

Core Functionality:

  1. User authentication system (JWT)
  2. Photo-based body measurement extraction
  3. 3D avatar generation with MakeHuman
  4. Size recommendation engine
  5. Virtual try-on with basic garment overlay
  6. Product catalog browsing

Advanced Features:

  • Measurement correction interface
  • Photo processing
  • Brand-specific size charts
  • Measurement history tracking
  • Responsive Flutter UI

Tech Stack #

Frontend:

  • Flutter (iOS/Android)
  • BLoC state management
  • Three.js for 3D rendering
  • ObjectBox for local caching

Backend:

  • Python FastAPI
  • PostgreSQL database
  • SQLAlchemy ORM
  • Redis caching
  • Docker containers

AI/ML Components:

  • MediaPipe for pose estimation
  • OpenCV for image processing
  • Random Forest models (scikit-learn)
  • MakeHuman for 3D modeling

Setup Instructions #

  1. Clone the repository:
git clone https://github.com/IU-Capstone-Project-2025/Looki.git
cd Looki
  1. Configure environment:
cp .env.example .env
# Edit .env with credentials
  1. Build and launch with Docker:
docker-compose up --build
  1. Access services:

Frontend: http://localhost

Backend API: http://localhost:8000

Swagger Docs: http://localhost:8000/docs

Presentation draft #

Presentation

Weekly commitments #

Individual contribution of each participant #

Ilya Maksimov

  • Implemented clothing try-on functionality for 3D models
  • Enhanced try_on screen with interactive clothing cards
  • Developed UI for selecting and applying garments to avatar

Aleksandr Gavrovskii

  • Added 3D model generation visualization
  • Implemented model regeneration with new body parameters
  • Redesigned body parameters screen for better UX

Nikita Shiyanov

  • Developed backend clothing attachment functionality
  • Optimized body generation parameters
  • Added new API endpoints for clothing management
  • Updated server-side MakeHuman for clothing integration
  • Implemented clothing-related database operations

Plan for Next Week #

Frontend Refinement #

  • UI/UX improvements for clothing selection interface
  • Performance optimization for 3D clothing rendering
  • Enhanced error handling during garment try-on
  • Virtual wardrobe organization system

AI Model Development #

  • Advanced garment fitting algorithms
  • Realistic cloth physics simulation
  • Improved body measurement accuracy
  • Clothing generation ML model:
    • Neural network for automatic garment fitting
    • Style transfer for clothing variations
    • Size adaptation algorithms
    • Texture and pattern generation

Confirmation of the code’s operability #

We confirm that the code in the main branch:

  • In working condition.
  • Run via docker-compose (or another alternative described in the README.md).