zoom smart virtual background package free download

So while the Avatars feature can tell what is or is not a face, it does not recognize or distinguish between individual faces. This feature does not use facial recognition, which means that it does not identify who you are.

Images of your face don’t leave your device when using this feat ure, and they are not stored or sent to Zoom. When you turn on this feature during a meeting, Zoom’s technology uses your device’s camera to detect where a face is on the screen and apply the selected avatar effect. Avatars also provide a good middle ground for users who don’t want to turn on their camera, but still want to leverage the power of body language and facial expressions. Avatars are an easy and entertaining way to engage with your meeting attendees and add a bit of fun. Live swap background by replacing file data/background.jpg - refresh rate hard coded at 3 seconds.Avatars enable you to replace your appearance with a 3D virtual avatar that mirrors your head movements and facial expressions. Launch zoom/teams/slack., select v4l2loopback as webcam

start the virtual camera via docker-compose (assuming physical video device at /dev/video0, virtual video device at /dev/video20 and gpu at /dev/nvidia0).

(optional) adjust camera resolution and fps in docker-compose.yml.

add root to group video (likely unecessary.).

Sudo modprobe v4l2loopback devices=1 video_nr=20 card_label='v4l2loopback' exclusive_caps=1 Sudo apt-get install -y v4l2loopback-dkms v4l2loopback-utils

install and setup virtual video device as '/dev/video20', and assuming the actual video device is '/dev/video0'.

(for those using GPU) test nvidia-docker is installed properlyĭocker run -gpus all nvidia/cuda:10.0-base nvidia-smi

(for those using GPU) install nvidia-docker ( ) I have tested the below instructions with Ubuntu 20.04, code used is archived in īranch master requires GPU, while branch cpu-friendly uses only CPU.ĮDIT: after getting the below to work, I also found out there is a more refined version by fangfufu which is also based on solution. The modified images are then used to create the video feed via pyfakewebcam and v4l2loopback.

For each image, the face/body is cropped using TensorFlow.js Bodypix, and merged with the specified background. What the code is doing essentially is grabbing image frames with Python and OpenCV. These instructions were originally posted by and IMO is a super fun and neat project, especially if you are into computer vision. The solution below helps achieve the similar effect, and since it creates a virtual webcam, you can most likely use this solution for any apps that use a webcam, for example, Microsoft Teams. Edit: Zoom 5.8.0 supports virtual background without greenscreen!Īs of writing this, Zoom does not support person/face-detection based virtual background in their Linux-version software.

Zoom smart virtual background package free download