OpenSeeFace VS mediapipe

I got something working modifying OpenSeeFace and it's an option and I might try to rewrite it in something compiled, but I'd like to look at the other options first.

Lastly, face tracking is either built in or uses a plugin device. You would also use OSC to manipulate blendshapes. I'd take a look at Opens Face.

Th best/only options I found were basically the old standby of Wine running closed source applications. Even then the end result was rather incomplete... because may of the best tracking options are simply closed source. (Note: Vseeface does offer an open source library)

There are only a few libraries that come to mind but take a bit of work to get started. MediaPipe Unity Plugin has eye tracking with a whole lot of types of tracking(head, hands, body). OpenSeeFace has models that do head and eye tracking. This repo uses Unity's neural net inference library, Barracuda, to run a MediaPipe iris landmark model (I haven't personally tested this library). Not sure how to translate eye landmarks to the position a player is looking at in a screen though. Hopefully this list of libraries gets you on the right path!

OpenSeeFace is open source. If you are using it with VSeeFace, you can just replace the Binary folder with your own build.

It uses OpenSeeFace for facetracking and engine patches/vrm code from the V-Sekai team.

mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub

Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.

You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md

I believe Google already released transformers under an apache 2 license with a patent grant:

https://github.com/google/mediapipe/blob/master/mediapipe/mo...

I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].

For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM

[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...

[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."

[3] - https://developers.google.com/mediapipe/solutions/vision/ima...

I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )

I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.

We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.

Medipaipe library link: https://mediapipe.dev/