AI-Powered Virtual Background and Blur for Video Calls

[miroslavpejic85]

Feature request

Implement AI-powered virtual background and blur functionality, allowing users to either replace their background with a custom image or blur it for enhanced privacy. This feature should work in real-time without requiring a green screen, ensuring smooth performance while maintaining video quality.

Pros

Pros of Virtual Background and Blur in Video Calls

✅ Privacy Protection

• Hides sensitive or personal surroundings, preventing unwanted background visibility.
• Useful for professionals working from home or public spaces.

✅ Professional Appearance

• Helps maintain a clean and distraction-free look, especially in business meetings.
• Can create a branded or customized background for companies.

✅ Focus on the Speaker

• Blurring reduces background distractions, keeping attention on the person speaking.
• Virtual backgrounds can be used to create a consistent visual experience.

✅ Enhanced User Experience

• Makes remote communication more engaging and fun.
• Users can personalize their background to suit different settings (casual, professional, themed).

✅ Flexible Environment Usage

• No need for a dedicated workspace—users can take calls from anywhere while maintaining a professional look.
• Helps in low-budget setups where users might not have an ideal background.

✅ Reduces the Need for Green Screens

• AI-based segmentation removes the need for physical green screens, making it accessible to everyone.

Additional context

Using MediaPipe can be a good solution.

✅ Pros of Using MediaPipe

1. Real-time Performance – Optimized for fast execution on both CPU and GPU.
2. No Green Screen Needed – Uses AI-based segmentation to separate the user from the background.
3. Cross-Platform Support – Works on web (JavaScript), mobile (Android/iOS), and desktop.
4. Lightweight & Efficient – Compared to heavier models like TensorFlow, MediaPipe is optimized for speed.
5. Google-backed & Actively Maintained – Reliable with frequent updates.
6. Works In-Browser – Can run client-side using WebAssembly (WASM) or WebGL, reducing server load.

❌ Potential Challenges

• Quality Variations – Might struggle with fine details like hair in low-light conditions.
• Performance on Low-End Devices – Can be CPU-intensive, so optimization is needed for weaker devices.
• Limited Customization – Pre-trained models may not be as flexible as custom ML solutions.

Alternative Solutions

• TensorFlow.js – More customizable but heavier.
• WebGPU/WebGL-based solutions – For better performance on supported devices.