Official PyTorch implementation for the paper:

EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation, ICCV 2023.

Ziqiao Peng, Haoyu Wu, Zhenbo Song, Hao Xu, Xiangyu Zhu, Jun He, Hongyan Liu, Zhaoxin Fan

Arxiv | Project Page | License

Given audio input expressing different emotions, EmoTalk produces realistic 3D facial animation sequences with corresponding emotional expressions as outputs.

• 2023.10.17 Thanks to noirmist! Now you can create the environment via docker.

• Linux
• Python 3.8.8
• Pytorch 1.12.1
• CUDA 11.3
• Blender 3.4.1
• ffmpeg 4.4.1

Clone the repo:

git clone https://github.com/psyai-net/EmoTalk_release.git
cd EmoTalk_release

Create conda environment:

conda create -n emotalk python=3.8.8
conda activate emotalk
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

Download Blender and put it in this directory.

wget https://ftp.nluug.nl/pub/graphics/blender/release/Blender3.4/blender-3.4.1-linux-x64.tar.xz
tar -xf blender-3.4.1-linux-x64.tar.xz
mv blender-3.4.1-linux-x64 blender && rm blender-3.4.1-linux-x64.tar.xz

Download the pretrained models from EmoTalk.pth (Updated). Put the pretrained models under pretrain_model folder. Put the audio under aduio folder and run

python demo.py --wav_path "./audio/disgust.wav"

The generated animation will be saved in result folder.

If someone wants to download the 3D-ETF dataset, please fill in the agreement, and use the education mailbox to email Ziqiao Peng ([email protected]) and cc Zhaoxin Fan ([email protected]) to request the download link.

If you find this work useful for your research, please cite our paper:

@InProceedings{Peng_2023_ICCV,
    author    = {Peng, Ziqiao and Wu, Haoyu and Song, Zhenbo and Xu, Hao and Zhu, Xiangyu and He, Jun and Liu, Hongyan and Fan, Zhaoxin},
    title     = {EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {20687-20697}
}

Here are some great resources we benefit:

• Faceformer for training pipeline
• EVP for training dataloader
• Speech-driven-expressions for rendering
• Wav2Vec2 Content and Wav2Vec2 Emotion for audio encoder
• Head Template for visualization.

Thanks to John Hable for sharing his head template under the CC0 license, which is very helpful for us to visualize the results.

For research purpose, such as comparison of experimental results, please contact [email protected]

For commercial licensing, please contact [email protected]

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. Please read the LICENSE file for more information.

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