SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces [ACM MM 2023]

Official PyTorch implementation for the paper:

SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces, ACM MM 2023.

Ziqiao Peng, Yihao Luo, Yue Shi, Hao Xu, Xiangyu Zhu, Hongyan Liu, Jun He, Zhaoxin Fan

Arxiv | Project Page | License

Given a speech signal as input, our framework can generate realistic 3D talking faces showing comprehensibility by recovering coherent textual information through the lip-reading interpreter and the speech recognizer.

Environment

• Linux
• Python 3.6+
• Pytorch 1.12.1
• CUDA 11.3
• ffmpeg
• MPI-IS/mesh

Clone the repo:

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

Create conda environment:

conda create -n selftalk python=3.8.8
conda activate selftalk
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

Dataset

VOCASET

Request the VOCASET data from https://voca.is.tue.mpg.de/. Place the downloaded files data_verts.npy, raw_audio_fixed.pkl, templates.pkl and subj_seq_to_idx.pkl in the folder vocaset/. Download "FLAME_sample.ply" from voca and put it in vocaset/. Read the vertices/audio data and convert them to .npy/.wav files stored in vocaset/vertices_npy and vocaset/wav:

cd vocaset
python process_voca_data.py

BIWI

Follow the BIWI/README.md to preprocess BIWI dataset and put .npy/.wav files into BIWI/vertices_npy and BIWI/wav, and the templates.pkl into BIWI/.

Demo

Download the pretrained models from BIWI.pth and vocaset.pth. Put the pretrained models under BIWI and VOCASET folders, respectively. Given the audio signal,

• to animate a mesh in FLAME topology, run:

   python demo_voca.py --wav_path "demo/wav/test.wav" --subject FaceTalk_170908_03277_TA
  

• to animate a mesh in BIWI topology, run:

   python demo_BIWI.py --wav_path "demo/wav/test.wav" --subject M1
  

  This script will automatically generate the rendered videos in the demo/output folder. You can also put your own test audio file (.wav format) under the demo/wav folder and specify the argument --wav_path "demo/wav/test.wav" accordingly.

Training and Testing on VOCASET

Data Preparation

• Read the vertices/audio data and convert them to .npy/.wav files stored in vocaset/vertices_npy and vocaset/wav:

   cd VOCASET
   python process_voca_data.py
  

Training and Testing

• To train the model on VOCASET, run:

   python main.py --dataset vocaset --vertice_dim 15069 --feature_dim 512 --period 30 --train_subjects "FaceTalk_170728_03272_TA FaceTalk_170904_00128_TA FaceTalk_170725_00137_TA FaceTalk_170915_00223_TA FaceTalk_170811_03274_TA FaceTalk_170913_03279_TA FaceTalk_170904_03276_TA FaceTalk_170912_03278_TA" --val_subjects "FaceTalk_170811_03275_TA FaceTalk_170908_03277_TA" --test_subjects "FaceTalk_170809_00138_TA FaceTalk_170731_00024_TA"
  

• To test the model on VOCASET, run:

  python test.py --dataset vocaset --vertice_dim 15069 --feature_dim 512 --period 30 --max_epoch 100 --train_subjects "FaceTalk_170728_03272_TA FaceTalk_170904_00128_TA FaceTalk_170725_00137_TA FaceTalk_170915_00223_TA FaceTalk_170811_03274_TA FaceTalk_170913_03279_TA FaceTalk_170904_03276_TA FaceTalk_170912_03278_TA" --val_subjects "FaceTalk_170811_03275_TA FaceTalk_170908_03277_TA" --test_subjects "FaceTalk_170809_00138_TA FaceTalk_170731_00024_TA"
  

  The results and the trained models will be saved to vocaset/result and vocaset/save.

Visualization

• To visualize the results, run:

   python render.py --dataset vocaset --vertice_dim 15069 --fps 30
  

  You can find the outputs in the vocaset/output folder.

Training and Testing on BIWI

Data Preparation

• Follow the BIWI/README.md to preprocess BIWI dataset.

Training and Testing

• To train the model on BIWI, run:

   python main.py --dataset BIWI --vertice_dim 70110 --feature_dim 1024 --period 25 --train_subjects "F2 F3 F4 M3 M4 M5" --val_subjects "F2 F3 F4 M3 M4 M5" --test_subjects "F2 F3 F4 M3 M4 M5"
  

• To test the model on BIWI, run:

  python test.py --dataset BIWI --vertice_dim 70110 --feature_dim 1024 --period 25 --max_epoch 100 --train_subjects "F2 F3 F4 M3 M4 M5" --val_subjects "F2 F3 F4 M3 M4 M5" --test_subjects "F2 F3 F4 M3 M4 M5"
  

  The results will be available in the BIWI/result folder. The trained models will be saved in the BIWI/save folder.

Visualization

• To visualize the results, run:

   python render.py --dataset BIWI --vertice_dim 70110 --fps 25
  

  The rendered videos will be available in the BIWI/output folder.

Using Your Own Dataset

Data Preparation

• Create the dataset directory <dataset_dir> in SelfTalk_release directory.

• Place your vertices data (.npy format) and audio data (.wav format) in <dataset_dir>/vertices_npy and <dataset_dir>/wav folders, respectively.

• Save the templates of all subjects to a templates.pkl file and put it in <dataset_dir>, as done for BIWI and vocaset. Export an arbitary template to .ply format and put it in <dataset_dir>/templates/.

Training and Testing

• Create the train, val and test splits by specifying the arguments --train_subjects, --val_subjects and --test_subjects in main.py.

• Train a SelfTalk model on your own dataset by specifying the arguments --dataset and --vertice_dim (number of vertices in your mesh * 3) in main.py. You might need to adjust --feature_dim and --period to your dataset. Run main.py.

• The results and models will be saved to <dataset_dir>/result and <dataset_dir>/save.

Visualization

• Specify the arguments --dataset, --vertice_dim and --fps in render.py. Run render.py to visualize the results. The rendered videos will be saved to <dataset_dir>/output.

Citation

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

  @inproceedings{peng2023selftalk,
    title={SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces}, 
    author={Ziqiao Peng and Yihao Luo and Yue Shi and Hao Xu and Xiangyu Zhu and Hongyan Liu and Jun He and Zhaoxin Fan},
    journal={arXiv preprint arXiv:2306.10799},
    year={2023}
  }

Acknowledgement

Here are some great resources we benefit:

• Faceformer for pipeline and readme
• CoderTalker for BIWI dataset preprocessing
• FaceXHuBERT for BIWI audio processing
• B3D(AC)2 and VOCASET for dataset
• Wav2Vec2 for audio encoder
• MPI-IS/mesh for mesh processing
• VOCA/rendering for rendering

Contact

For research purpose, please contact [email protected]

For commercial licensing, please contact [email protected]

License

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

Invitation

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