This is our official implementation of the paper

"GaussianTalker: Real-Time High-Fidelity Talking Head Synthesis with Audio-Driven 3D Gaussian Splatting"

by Kyusun Cho*, Joungbin Lee*, Heeji Yoon*, Yeobin Hong, Jaehoon Ko, Sangjun Ahn, Seungryong Kim^†

❗️2024.06.13: We also generated the torso in the same space as the face using Gaussian splatting. After cloning the torso branch, you can train and render it in the same way to use it.

For more information, please check out our Paper and our Project page.

We implemented & tested GaussianTalker with NVIDIA RTX 3090 and A6000 GPU.

Run the below codes for the environment setting. ( details are in requirements.txt )

git clone https://github.com/joungbinlee/GaussianTalker.git
cd GaussianTalker
git submodule update --init --recursive
conda create -n GaussianTalker python=3.7 
conda activate GaussianTalker

pip install -r requirements.txt
pip install -e submodules/custom-bg-depth-diff-gaussian-rasterization
pip install -e submodules/simple-knn
pip install "git+https://github.com/facebookresearch/pytorch3d.git"
pip install tensorflow-gpu==2.8.0
pip install --upgrade "protobuf<=3.20.1"

We used talking portrait videos from AD-NeRF, GeneFace and HDTF dataset. These are static videos whose average length are about 3~5 minutes.

You can see an example video with the below line:

wget https://github.com/YudongGuo/AD-NeRF/blob/master/dataset/vids/Obama.mp4?raw=true -O data/obama/obama.mp4

We also used SynObama for cross-driven setting inference.

• prepare face-parsing model.

wget https://github.com/YudongGuo/AD-NeRF/blob/master/data_util/face_parsing/79999_iter.pth?raw=true -O data_utils/face_parsing/79999_iter.pth

• Download 3DMM model from Basel Face Model 2009

Put "01_MorphableModel.mat" to data_utils/face_tracking/3DMM/

cd data_utils/face_tracking
python convert_BFM.py
cd ../../
python data_utils/process.py ${YOUR_DATASET_DIR}/${DATASET_NAME}/${DATASET_NAME}.mp4 

• Obtain AU45 for eyes blinking

Run FeatureExtraction in OpenFace, rename and move the output CSV file to (your dataset dir)/(dataset name)/au.csv.

├── (your dataset dir)
│   | (dataset name)
│       ├── gt_imgs
│           ├── 0.jpg
│           ├── 1.jgp
│           ├── 2.jgp
│           ├── ...
│       ├── ori_imgs
│           ├── 0.jpg
│           ├── 0.lms
│           ├── 1.jgp
│           ├── 1.lms
│           ├── ...
│       ├── parsing
│           ├── 0.png
│           ├── 1.png
│           ├── 2.png
│           ├── 3.png
│           ├── ...
│       ├── torso_imgs
│           ├── 0.png
│           ├── 1.png
│           ├── 2.png
│           ├── 3.png
│           ├── ...
│       ├── au.csv
│       ├── aud_ds.npy
│       ├── aud_novel.wav
│       ├── aud_train.wav
│       ├── aud.wav
│       ├── bc.jpg
│       ├── (dataset name).mp4
│       ├── track_params.pt
│       ├── transforms_train.json
│       ├── transforms_val.json

python train.py -s ${YOUR_DATASET_DIR}/${DATASET_NAME} --model_path ${YOUR_MODEL_DIR} --configs arguments/64_dim_1_transformer.py 

Please adjust the batch size to match your GPU settings.

python render.py -s ${YOUR_DATASET_DIR}/${DATASET_NAME} --model_path ${YOUR_MODEL_DIR} --configs arguments/64_dim_1_transformer.py --iteration 10000 --batch 128

Please locate the files <custom_aud>.wav and <custom_aud>.npy in the following directory path: ${YOUR_DATASET_DIR}/${DATASET_NAME}.

python render.py -s ${YOUR_DATASET_DIR}/${DATASET_NAME} --model_path ${YOUR_MODEL_DIR} --configs arguments/64_dim_1_transformer.py --iteration 10000 --batch 128 --custom_aud <custom_aud>.npy --custom_wav <custom_aud>.wav --skip_train --skip_test

If you find our work useful in your research, please cite our work as:

@misc{cho2024gaussiantalker,
      title={GaussianTalker: Real-Time High-Fidelity Talking Head Synthesis with Audio-Driven 3D Gaussian Splatting}, 
      author={Kyusun Cho and Joungbin Lee and Heeji Yoon and Yeobin Hong and Jaehoon Ko and Sangjun Ahn and Seungryong Kim},
      year={2024},
      eprint={2404.16012},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}