Hello, I am very interested in segmentation models.
The reason I'm interested in segmentation model is that it can automate layer separation in Spine 2D.

I have created a project that utilizes the Dlib model to parse faces and create a layer format in GIMP.

https://github.com/jhj0517/Dlib-Face-Parsing-WebUI

The segmentation range recognized by Dlib model includes:

[Jaw line , Right eyebrow , Left eyebrow , Nose , Right eye , Left eye , Lips]

My project automatically parses these parts of the face from an input image, separates them into individual images, and combines them into a .tif file that can be opened in GIMP.

To make it easy to use, I made it using the Gradio package, which makes it easy to use on the Web-UI, and also made it easy to install and run with a batch file (bash for Linux).

Detailed installation and usage instructions can be found in my Github repository.

I believe Spine 2D is the most convenient program for creating animations.
However, separating layers from an existing image (instead of drawing them separately from the beginning) can be very challenging for people who are not professionals in drawing, just like me.

Therefore, I thought it would be great to have a project that automates this process.

However, Dlib may not be the ideal model for my project due to its limited range of segmentation types and lower-than-expected accuracy. Additionally, as it is trained on real human faces, the model does not perform well in recognizing the faces of animated characters, which is a significant drawback.
and most importantly, it is limited to facial recognition.

I am currently studying Controlnet to find a better model to achieve this cool feat. (I'm looking for an appropriate parsing model)
I am working on a project that can perform parsing properly as I want.

I hope my project can be helpful to Spine 2D users who have difficulty with layer separation like myself.
Thank you for reading.