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https://ku-cvlab.github.io/MoDiTalker/


https://ku-cvlab.github.io/MoDiTalker/

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README 

        
# MoDiTalker



Official PyTorch implementation of **["MoDiTalker: Motion-Disentangled Diffusion

Model for High-Fidelity Talking Head Generation"](https://arxiv.org/abs/2403.19144)**.   



Seyeon Kim∗1, 

Siyoon Jin∗1, 

Jihye Park∗1, 

Kihong Kim2,

Jiyoung Kim1,

Jisu Nam1 and

Seungryong Kim†1.




∗ Equal contribution ,†Corresponding author




1Korea University, 2VIVE STUDIO  

[paper](https://arxiv.org/abs/2403.19144) | [project page](https://ku-cvlab.github.io/MoDiTalker/)



## 1. Environment setup



```bash

conda create -n MoDiTalker python=3.8 -y

conda activate MoDiTalker

python -m pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

python -m pip install natsort tqdm gdown omegaconf einops lpips pyspng tensorboard imageio av moviepy numba p_tqdm soundfile face_alignemnt

```



## 2. Get ready to train models 



### 2.1. Dataset 



We utilized two datasets for training each stage. 

Please refer and follow the dataset preparation from [here](https://github.com/KU-CVLab/MoDiTalker/data/README.md)



### 2.2. Download auxiliary models



Get the [`BFM_model_front.mat`](https://drive.google.com/file/d/1d08qauPUH0Nu_yN2gcmreLSiOiweD5OE/view?usp=sharing), [`similarity_Lm3D_all.mat`](https://drive.google.com/file/d/17zp_zuUYAuieCWXerQkbp8SRSU4KJ8Fx/view?usp=sharing) and [`Exp_Pca.bin`](https://drive.google.com/file/d/1SPeJ4jcJT9VS4IdA7opzyGHCYMKuCLRh/view?usp=sharing), and place them to the `MoDiTalker/data/data_utils/deep_3drecon/BFM` directory.

Obtain ['BaselFaceModel.tgz](https://drive.google.com/file/d/1Kogpizrcf2zTm1fX9uUUWZuMQqHM7DOc/view?usp=sharing) and extract a file named `01_MorphableModel.mat` and place it to the `MoDiTalker/data/data_utils/deep_3drecon/BFM` directory.



#### (Optional) 

We had to revise a single code inside the package `accelerate` due to version conflicts. If some conflicts occur during loading data, please revise the code in `accelerate/dataloader.py` following this;



from 

```bash

batch_size = dataloader.batch_size if dataloader.batch_size is not None else dataloader.batch_sampler.batch_size

```

to

```bash

batch_size = dataloader.batch_size if dataloader.batch_size is not None else len(dataloader.batch_sampler[0])

```



## 3. Training



### 3.1. AToM



```bash

cd AToM

bash scripts/train.sh

```

The checkpoints of AToM will be saved in `./runs`



### 3.2. MToV

The checkpoints of AToM will be saved in `./runs`



### Autoencoder



First, execute the following script:

```bash

cd MToV

bash scripts/train/first_stg.sh 

```

Then the script will automatically create the folder in `./log_dir` to save logs and checkpoints.



Second, execute the following script:

```bash

cd MToV

bash scripts/train/first_stg_ldmk.sh 

```

You may change the model configs via modifying `configs/autoencoder`. Moreover, one needs early-stopping to further train the model with the GAN loss (typically 8k-14k iterations with a batch size of 8).



### Diffusion model

```bash

cd MToV

bash scripts/train/second_stg.sh

```



### 4. Getting the Weights

We provide the corresponding checkpoints in the below:

Download and place them in the `./checkpoints/` directory. 



Full checkpoints will be released later, ETA July 2024.



### 5. Inference

#### 5.1. Generating Motions from Audio 

Before producing the motions from audio, there's need to preprocess the audio since we process audio in the type of HuBeRT. To produce hubert feature of audio you want, please follow the script below:



```bash

cd data

python data_utils/preprocess/process_audio.py \

--audio path to audio \

--ref_dir path to directory of reference images 

```



Then the processed audio hubert(npy) will be saved in `data/inference/hubert/{sampling rate}` 



Note that, you need to specify the path to (1) reference images (2) processed hubert and (3) checkpoint in the following bash script. 



```bash

cd AToM

bash scripts/inference.sh

```



The results of AToM will be saved in `AToM/results/frontalized_npy` and this path should be consistent with the `ldmk_path` of the following step.



#### 5.2. Align Motions

Note that, you need to specify the path to (1) reference images and (2) produced landmark. 



```bash 

cd data/data_utils

python motion_align/align_face_recon.py \

--ldmk_path path to directory of generated landmark \

--driv_video_path path to directory of reference images 

```

The final landmarks will be saved in `AToM/results/aligned_npy`.



#### 5.3. Generating Video from aligned Motions

```bash 

cd MToV

bash scripts/inference/sample.sh

```

The final videos will be saved in `MToV/results`.



### Citation

```bibtex

@misc{kim2024moditalker,

      title={MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation}, 

      author={Seyeon Kim and Siyoon Jin and Jihye Park and Kihong Kim and Jiyoung Kim and Jisu Nam and Seungryong Kim},

      year={2024},

      eprint={2403.19144},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```



### Reference

This code is mainly built upon [EDGE](https://github.com/Stanford-TML/EDGE) and [PVDM](https://github.com/sihyun-yu/PVDM/tree/main).\

We also used the code from following repository: [GeneFace](https://github.com/yerfor/GeneFace).