For current SOTA please use the following instead https://github.com/KwaiVGI/LivePortrait

This repository focuses on portrait animation, specifically lip-synchronization via 3DMM control, but also allows for video-driven animation.

I will update this repo with correct layout and intruction but this has been tested on python 3.10 with cuda 11.8 in annaconda windows

Please complete the following steps.

Clone the repository:

git clone https://github.com/Inferencer/SickFace.git
cd SickFace

We recommend to create a new conda environment:

conda create -n sickface python=3.10
conda activate sickface

This code requires at least Python 3.10 and PyTorch.

1. Install PyTorch (>= 1.12.0)

   conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
   

2. Additional dependencies can be installed via:

   pip install -r requirements.txt
   

3. Run the gradio ui

python app.py

Alternatively you can console commands using the following

python demo.py --checkpoint checkpoints/vox256.pt --config ./configs/vox256.yaml  --source_images ./examples/myimagefile.jpg --driving_video ./examples/mydriveingfile.mp4 --relative --adapt_scale --find_best_frame --audio

If you wish to use more than one source image you are welcome to use up to two in total by adding another image path such as

python demo.py --checkpoint checkpoints/vox256_2Source.pt --config ./configs/vox256.yaml  --source_images ./examples/myimagefile.jpg ./examples/myimagefile2.jpg --driving_video ./examples/drive.mp4 --relative --adapt_scale --find_best_frame --audio

You can use different file formats such as .png if you so wish but before using multiple source image I reccomend reading this issue you will also notice the checkpoint has changed to vox256_2Source.pt

Pretrained models can be found at google-drive.

The models should be downloaded and placed in ./checkpoints so checkpoints/kp_detector.pt & checkpoints/vox256.pt. Note that all pretrained checkpoints are trained using the same keypoint detector weights.

• Code Base: Uses code from FSRT.
• Enhancements:
  • Added upscaling implementation.
  • UI
  • Possible further training with the vox512 dataset or another dataset (not sure why vox256 has been the default in recent years.)
• 3DMM Selection: The 3DMM to be used is yet to be decided from the following list:
  • CodeTalker
  • EmoTalk
  • Emote
  • FaceDiffuser
  • FaceFormer

• Upgrades: Will incorporate advancements from InvertAvatar, Portrait-4dv2, or another state-of-the-art (SOTA) model released this year.
• 3DMM Upgrade: Potential integration of the upcoming Media2Face.

• Future Goals:
  • Expected release by late 2025.
  • Aim for Gaussian-based methods.
  • Focus on one-shot methods with minimal training requirements.
  • Training constraints: Should not exceed 1 hour on an A100 GPU and should use a maximum of 30 seconds of video identity data.

SickFace aims to push the boundaries of portrait animation by leveraging state-of-the-art techniques and efficient training methods. Stay tuned for updates as we progress through each version!