video-avatars-and-virtual-backgrounds

To implement virtual backgrounds for WebRTC video streams, you apply selfie segmentation and canvas-based WebGL compositing to isolate the subject and replace the background scene with low latency. This pipeline processes webcam frames before streaming.

Face mesh tracking maps facial landmarks to animate 2D or 3D avatars during live video processing. It tracks facial movements in real-time, allowing the rendered avatar to mimic the user's expressions and head pose for conferencing apps.

Yes, WebGL is used for low-latency frame processing by compositing video frames and applying effects like background blur or replacement. It handles the graphical rendering pipeline efficiently to maintain real-time performance across web and desktop environments.

Yes, selfie segmentation isolates the foreground subject to apply background blur, while reverse segmentation isolates the background itself. Both techniques are supported to enable background replacement or targeted scene effects in live video feeds.

Performance tuning requires optimizing low-latency frame processing and WebRTC integration to handle video streams efficiently across devices and networks. It involves pipeline mapping and edge-case handling to maintain smooth real-time rendering.