mediapipe VS yoha

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/

Hey, thank you for this question. You basically want to be able to use hand tracking like a computer mouse (please correct me if I'm wrong). My gut feel is that this would not be too hard to do. If the major operating systems offer the respective APIs to do so (which I would assume they do) then it should be mainly a matter of putting in the work to implement it. If you want to, feel free to open an issue on GitHub to document this feature request.

GitHub - handtracking-io/yoha: A practical hand tracking engine.

Demo (Code)

Thank you for the feedback. I can confirm there seems to be an issue with iOS/Chrome and created a GitHub issue for it (https://github.com/handtracking-io/yoha/issues/5).

Note that if you were trying iOS/Safari and not iOS/Chrome there is nothing that can be done due to a limitation that is documented in the section "Discussion" here: https://developer.apple.com/documentation/webkitjs/canvasren... Will document this.