Imitator: Personalized Speech-driven 3D Facial Animation

ICCV 2023

Imitator
Balamurugan Thambiraja, Ikhsanul Habibie, Sadegh Aliakbarian, Darren Cosker, Christian Theobalt, Justus Thies

arXiv | BibTeX | Project Page

News

• Accepted to ICCV 2023
• Sep 27, 2023: Code uploaded.
• Oct 16, 2023: Code issues fixed and added support for windows, Thanks to sihangchen97

To-Do

• Releasing 3 personalized model[Boris, Kamala Harris, Trevor Noah] for style adaptation comparison on the external actors. (If you need it immediately for upcoming CVPR 2024 deadline, please write me a mail)

Installation

Linux:

git clone https://github.com/bala1144/Imitator.git
cd Imitator
bash install.sh

Windows:

git clone https://github.com/bala1144/Imitator.git
cd Imitator
install.bat

Mac:

Follow the linux commands and use the dowload_asset.sh. Make sure to install wget, except change line 7 of install.sh to the version of python your machine is running.

Download assests (Pretrained models, masks etc)

Linux and Mac:

bash download_asset.sh

Windows:

download_asset.bat

We will update 3 pretrained models

• generalized_model_mbp (Generalized model on VOCAset)
• subj0024_stg02_04seq (FaceTalk_170731_00024_TA personalized model)
• subj0138_stg02_04seq (FaceTalk_170731_00024_TA personalized model)

Data Preparation

VOCA

Download the VOCA and prepare using the script from Faceformer

git clone https://github.com/EvelynFan/FaceFormer.git
cd FaceFormer/vocaset
python process_voca_data.py
cd ../..

wav2vec model (optional, for offline use)

Download wav2vec model, for example wav2vec2-base-960h from HuggingFace.

Overriding config file (optional)

The directories of VOCA dataset and wav2vec model might differ from one to one.

Set environment variables (VOCASET_PATH, WAV2VEC_PATH) to run test scripts mentioned above, if you find it hard to find or modify config files (*.yaml).

Linux:

export VOCASET_PATH=<Path to vocaset folder>
export WAV2VEC_PATH=<Path to wav2vec model folder (e.g. wav2vec2-base-960h)>

Windows:

set VOCASET_PATH=<Path to vocaset folder>
set WAV2VEC_PATH=<Path to wav2vec model folder (e.g. wav2vec2-base-960h)>

Testing

on external audio

To evaluate the external audio, you can use the demo audio on the assets/demo/

python imitator/test/test_model_external_audio.py -m pretrained_model/generalized_model_mbp_vel --audio assets/demo/audio1.wav -t FaceTalk_170731_00024_TA -c 2 -r -d 

• -a path to the audio file
• -t specify the subject of the VOCA
• -c specify the condition[0,1...7] from VOCA used for testing
• -r to render the results as videos
• -d to dump the prediction as npy files

on VOCA

To evaluate the generalized/personalized model on VOCA

python imitator/test/test_model_voca.py -m pretrained_model/generalized_model_mbp_vel -r -d
python imitator/test/test_model_voca.py -m pretrained_model/subj0024_stg02_04seq -r -d
python imitator/test/test_model_voca.py -m pretrained_model/subj0138_stg02_04seq -r -d

• -c to specify the config of the dataset; edit the imitator/test/data_cfg.yml to point to your dataset path.

Training models

Generalized

Train the generalized model on VOCA with

python main.py -b cfg/generalized_model/imitator_gen_ab_mbp_vel10.yaml --gpus 0,

Personalized model

First stage of the personalization, we optimize for the Style code using model from generalized model

python main.py -b cfg/style_adaption/subj0024_stg01_04seq.yaml --gpus 0,

Second stage of the personalization, we optimize for the Style code and Displacements using the model from Stage 01

python main.py -b cfg/style_adaption/subj0024_stg02_04seq.yaml --gpus 0,

More

Shout-outs

Thanks to everyone who makes their code and models available. In particular,

• The architecture of our method is inspired by Faceformer
• Our repo is built on top of the repos of Taming_Tranformers and Faceformer
• related works Faceformer, codetalker, VOCA and Meshtalk

BibTeX

@InProceedings{Thambiraja_2023_ICCV,
    author    = {Thambiraja, Balamurugan and Habibie, Ikhsanul and Aliakbarian, Sadegh and Cosker, Darren and Theobalt, Christian and Thies, Justus},
    title     = {Imitator: Personalized Speech-driven 3D Facial Animation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {20621-20631}
}