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This repository was archived by the owner on Sep 1, 2021. It is now read-only.
/VTuber_UnityPublic archive

Use Unity 3D character and Python deep learning algorithms to stream as a VTuber!

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kwea123/VTuber_Unity

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2021/9/1: Due to massive bugs and the fast-moving virtual character technology, I decide to archive this repository (no updates anymore). Nowadays you can basically find many public tools on the internet, even for mobile platforms. This repository doesn't work as well as those tools, but can still serve as a tool if you want to integrate your character in Unity and custom it (e.g. play around with Unity objects).

Use Unity 3D character and Python deep learning algorithms to stream as a VTuber!

This is part of theOpenVTuberProject, which provides many toolkits for becoming a VTuber.

Youtube Playlist (Chinese) (Covers videos 1-4):teaser


Credits

First of all, I'd like to give credits to the following projects that I borrow code from:

ProjectLICENSE
head-pose-estimationLICENSE
face-alignmentLICENSE
GazeTrackingLICENSE

And the virtual characterunity-chan © UTJ/UCL.

Installation

Hardware

  • OS: Ubuntu 16.04 (18.04 may also work) or Windows 10 64bits or MacOS
  • (Optional but recommended) An NVIDIA GPU (tested with CUDA 9.0, 10.0 and 10.1, but may also work with other versions)

Software

  • Python3.x (installation viaAnaconda is recommended;mandatory for Windows users)

    • (Optional) It is recommended to use conda environments. Runconda create -n vtuber python=3.6. Activate it byconda activate vtuber.
  • Python libraries

    • Ubuntu:
      • Install the requirements bypip install -r requirements_(cpu or gpu).txt
      • If you have CUDA 10.1,pip install onnxruntime-gpu to get faster inference speed using onnx model.
    • Windows:
      • CPU:
        • pip install -r requirements_cpu.txt
        • ifdlib cannot be properly installed, followhere.
      • GPU:
        • Installpytorch usingconda. Example:conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
        • Install other dependencies bypip install -r requirements_gpu.txt.
        • If you have CUDA 10,pip install onnxruntime-gpu to get faster inference speed using onnx model.
  • Optional

Example usage

Here we assume that you have installed the requirements and activated the virtual environment you are using.

0. Model download

You need to download the modelshere, extract and put intoface_alignment/ckpts.

If you don't useonnxruntime, you can omit this step as the script will automatically download them for you.

1. Face detection test

Runpython demo.py --debug. (add--cpu if you have CPU only)

You should see the following:


Left: CPU model. Right: GPU model run on a GTX1080Ti.

2. Synchronize with the virtual character

  1. Download and launch the binarieshere depending on your OS to launch the unity window featuring the virtual character (unity-chan here).Important: Ensure that only one window is opened at a time!
  2. After the vitual character shows up, runpython demo.py --connect to synchronize your face features with the virtual character. (add--debug to see your face and--cpu if you have CPU only as step 1.)

You should see the following:


Left: CPU model. Right: GPU model run on a GTX1080Ti.

Enjoy your VTuber life!

Functionalities details

In this section, I will describe the functionalities implemented and a little about the technology behind.

Head pose estimation

Usinghead-pose-estimation andface-alignment, deep learning methods are applied to do the following: face detection and facial landmark detection. A face bounding box and the 68-point facial landmark is detected, then a PnP algorithm is used to obtain the head pose (the rotation of the face). Finally, kalman filters are applied to the pose to make it smoother.

The character's head pose is synchronized.

As for the visualization, the white bounding box is the detected face, on top of which 68 green face landmarks are plotted. The head pose is represented by the green frustum and the axes in front of the nose.

Gaze estimation

UsingGazeTracking, The eyes are first extracted using the landmarks enclosing the eyes. Then the eye images are converted to grayscale, and a pixel intensity threshold is applied to detect the iris (the black part of the eye). Finally, the center of the iris is computed as the center of the black area.

The character's gaze is not synchronized. (Since I didn't find a way to move unity-chan's eyes)

As for the visualization, the red crosses indicate the iris.

Miscellaneous

  1. Estimateeye aspect ratio: The eye aspect ratio can be used to detect blinking, but currently I just use auto blinking since this estimation is not so accurate.

  2. Estimatemouth aspect ratio: I use this number to synchronize with the character's mouth.

  3. The mouth distance is used to detect smile and synchronize with the character.

Unity Project

If you want to customize the virtual character, you can find the unity project inrelease.

License

MIT License

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