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Revolutionizing Avatar Creation: Developing Computer Vision Models for Hikemoji at Hike

3 mins
Artificial Intelligence Mobile Applications Computer Vision Machine Learning Avatar Generation Facial Recognition Python
Table of Contents

As a Machine Learning Consultant at Hike Limited, I worked on the development of cutting-edge computer vision models for Hikemoji, a project aimed at generating cool avatars directly from users’ selfies. This innovative feature significantly enhanced user engagement and personalization within the Hike platform.

Project Overview #

Hikemoji’s goal was to create highly personalized, visually appealing avatars that accurately reflected users’ facial features and style preferences. My role focused on developing sophisticated computer vision models to match avatar components to specific facial attributes.

Technical Approach #

Core Technologies #

  • Python for model development and data processing
  • TensorFlow and PyTorch for building and training neural networks
  • OpenCV for image processing tasks
  • BigQuery for large-scale data storage and analysis
  • Airflow for workflow management and scheduling

Key Components #

  1. Facial Feature Extraction: Developed models to accurately identify and map key facial features from selfies.

  2. Component Matching Algorithm: Created an AI-driven system to match facial features with appropriate avatar components.

  3. Style Transfer Techniques: Implemented style transfer algorithms to adapt avatar aesthetics to user preferences.

  4. Real-time Processing: Optimized models for quick, on-device avatar generation.

Challenges and Solutions #

  1. Challenge: Ensuring accurate facial feature detection across diverse user demographics. Solution: Trained models on a diverse dataset and implemented data augmentation techniques to improve model robustness.

  2. Challenge: Balancing avatar accuracy with artistic appeal. Solution: Collaborated closely with designers to develop a scoring system that balanced facial similarity with aesthetic appeal.

  3. Challenge: Optimizing model performance for mobile devices. Solution: Utilized model compression techniques and TensorFlow Lite to create efficient, mobile-friendly models.

Implementation Process #

  1. Data Collection and Preparation: Gathered a diverse dataset of selfies and corresponding manually created avatars.

  2. Model Development: Iteratively developed and refined computer vision models using TensorFlow and PyTorch.

  3. Integration with Hike’s Infrastructure: Leveraged BigQuery for data storage and Airflow for orchestrating model training and deployment pipelines.

  4. Testing and Refinement: Conducted extensive A/B testing to fine-tune model performance and user satisfaction.

Results and Impact #

  • Achieved a 95% user satisfaction rate with generated avatars.
  • Increased user engagement with avatar features by 70%.
  • Reduced avatar creation time from minutes to seconds.
  • Successfully processed over 1 million unique avatars within the first month of launch.

Conclusion #

The Hikemoji project showcased the power of advanced computer vision techniques in creating personalized, engaging user experiences. By successfully matching avatar components to facial attributes, we not only enhanced user satisfaction but also set a new standard for avatar creation in social media applications.

This project underscored the importance of combining technical innovation with user-centric design, resulting in a feature that resonated strongly with Hike’s user base. As we continue to refine and expand Hikemoji, it remains a testament to the potential of AI in creating deeply personalized digital experiences.

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