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hififace

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Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

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Download Latest Version (.zip)

- - base_dataset.py
- - __init__.py
  - bfm.py
  - facerecon_model.py
- - load_mats.py
- - 01_source.png
  - 01_target.png
  - 02_source.png
  - 02_target.png
  - 03_source.png
  - 03_target.png
- front_final.png
- grid_results.png
- hififace_arch_500.png
- obama_trump_biden.gif
- model.yaml
- trainer.yaml
- 10_nvidia.json
- hififace.py
- loss.py
- multiscalediscriminator.py
dataset.py
Dockerfile
hififace_inference.py
hififace_pl.py
hififace_trainer.py
LICENSE
README.md
requirements.txt
util.py

# Installation Guide

1. Get the code

git clone https://github.com/maum-ai/hififace

Downloads the entire project code from GitHub to your computer.

cd hififace

Moves into the project folder you just downloaded.

2. Docker

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

docker build -t hififace:latent .

Builds a runnable image based on the Dockerfile.

docker run -it --ipc host --gpus all -v /PATH_TO/hififace:/workspace -v /PATH_TO/DATASET/FOLDER:/DATA --name hififace hififace:latent

Runs the built image as an actual container.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# HifiFace &mdash; Unofficial Pytorch Implementation
>![](https://raw.githubusercontent.com/maum-ai/hififace/master/assets/front_final.png)
>
> Image source: *[HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping](https://arxiv.org/abs/2106.09965)* (figure 1, pg. 1)

![issueBadge](https://img.shields.io/github/issues/mindslab-ai/hififace)   ![starBadge](https://img.shields.io/github/stars/mindslab-ai/hififace)   ![repoSize](https://img.shields.io/github/repo-size/mindslab-ai/hififace)  ![lastCommit](https://img.shields.io/github/last-commit/mindslab-ai/hififace)

This repository is an unofficial implementation of the face swapping model proposed by _Wang et. al_ in their paper [HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping](https://arxiv.org/abs/2106.09965). This implementation makes use of the [Pytorch Lighting](https://www.pytorchlightning.ai/) library, a light-weight wrapper for [PyTorch](https://pytorch.org/).

## HifiFace Overview
The task of face swapping applies the face and the identity of the source person to the head of the target.

The HifiFace architecture can be broken up into three primary structures. The _3D shape-aware identity extractor_, the _semantic facial fusion module_, and an encoder-decoder structure. A high-level overview of the architecture can be seen in the image below.

> ![](https://raw.githubusercontent.com/maum-ai/hififace/master/assets/hififace_arch_500.png)
>
> Image source: *[HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping](https://arxiv.org/abs/2106.09965)* (figure 2, pg. 3)

## Changes from the original paper
### Dataset
In the paper, the author used VGGFace2 and Asian-Celeb as the training dataset. Unfortunately, the Asian-Celeb dataset can only be accessed with a Baidu account, which we do not have. Thus, we only use VGGFace2 for our training dateset.
### Model
The paper proposes two versions of HifiFace model based on the output image size: 256x256 and 512x512 (referred to as Ours-256 and Ours-512 in the paper). The 512x512 model uses an extra data preprocessing before training. In this open source project, we implement the 256x256 model.
For the discriminator, the original paperuses the discriminator from [StarGAN v2](https://arxiv.org/abs/1912.01865). Our implementation uses the multi-scale discriminator from [SPADE](https://arxiv.org/abs/1903.07291).

## Installation

### Build Docker Image
```shell
git clone https://github.com/mindslab-ai/hififace 
cd hififace
git clone https://github.com/sicxu/Deep3DFaceRecon_pytorch && git clone https://github.com/NVlabs/nvdiffrast && git clone https://github.com/deepinsight/insightface.git
cp -r insightface/recognition/arcface_torch/ Deep3DFaceRecon_pytorch/models/
cp -r insightface/recognition/arcface_torch/ ./model/
rm -rf insightface
cp -rf 3DMM/* Deep3DFaceRecon_pytorch
mv Deep3DFaceRecon_pytorch model/
rm -rf 3DMM
docker build -t hififace:latent .
rm -rf nvdiffrast
```
This Dockerfile was inspired by [@yuzhou164](https://github.com/yuzhou164), [this](https://github.com/sicxu/Deep3DFaceRecon_pytorch/issues/2#issuecomment-884087625) issue from [Deep3DFaceRecon_pytorch](https://github.com/sicxu/Deep3DFaceRecon_pytorch).

### Pre-Trained Model for Deep3DFace PyTorch
Follow the guideline in [Prepare prerequisite models](https://github.com/sicxu/Deep3DFaceRecon_pytorch#prepare-prerequisite-models)

Set up at `./mode/Deep3DFaceRecon_pytorch/`

### Pre-Trained Models for ArcFace
We used official Arcface per-trained pytorch [implementation](https://github.com/deepinsight/insightface/tree/415da817d127319a99aeb84927f2cd0fcbb3366c/recognition/arcface_torch)
Download pre-trained checkpoint from [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d) (IResNet-100 trained on MS1MV3)

### Download HifiFace Pre-Trained Model
[google drive link](https://drive.google.com/file/d/1tZitaNRDaIDK1MPOaQJJn5CivnEIKMnB/view?usp=sharing)
trained on VGGFace2, 300K iterations

## Training
### Dataset & Preprocessing
#### Align & Crop
We aligned the face images with the landmark extracted by [3DDFA_V2](https://github.com/cleardusk/3DDFA_V2). The code will be added.

#### Face Segmentation Map
After finishing aligning the face images, you need to get the face segmentation map for each face images. We used face segmentation model that [PSFRGAN](https://github.com/chaofengc/PSFRGAN) provides. You can use their code and pre-trained model.

#### Dataset Folder Structure
Each face image and the corresponding segmentation map should have the same name and the same relative path from the top-level directory.
```python
face_image_dataset_folder
└───identity1
│   │   image1.png
│   │   image2.png
│   │   ...
│   
└───identity2
│   │   image1.png
│   │   image2.png
│   │   ...
│ 
|   ...

face_segmentation_mask_folder
└───identity1
│   │   image1.png
│   │   image2.png
│   │   ...
│   
└───identity2
│   │   image1.png
│   │   image2.png
│   │   ...
│ 
|   ...
```

### Wandb
[Wandb](https://www.wandb.com/) is a powerful tool to manage your model training. 
Please make a wandb account and a wandb project for training HifiFace with our training code.

### Changing the Configuration
* [config/model.yaml](config/model.yaml)
    * dataset.train.params.image_root: directory path to the training dataset images
    * dataset.train.params.parsing_root: directory path to the training dataset parsing images
    * dataset.validation.params.image_root: directory path to the validation dataset images
    * dataset.validation.params.parsing_root: directory path to the validation dataset parsing images

* [config/trainer.yaml](config/trainer.yaml)
    * checkpoint.save_dir: directory where the checkpoints will be saved
    * wandb: fill out your wandb entity and project name

### Run Docker Container
```shell
docker run -it --ipc host --gpus all -v /PATH_TO/hififace:/workspace -v /PATH_TO/DATASET/FOLDER:/DATA --name hififace hififace:latent
```

### Run Training Code
```shell
python hififace_trainer.py --model_config config/model.yaml --train_config config/trainer.yaml -n hififace
```
## Inference
### Single Image
```shell
python hififace_inference --gpus 0 --model_config config/model.yaml --model_checkpoint_path hififace_opensouce_299999.ckpt --source_image_path assets/inference_samples/01_source.png --target_image_path assets/inference_samples/01_target.png --output_image_path ./01_result.png
```
### All Posible Pairs of Images in Directory 
```shell
python hififace_inference --gpus 0 --model_config config/model.yaml --model_checkpoint_path hififace_opensouce_299999.ckpt  --input_directory_path assets/inference_samples --output_image_path ./result.png
```
### Interpolation
```shell
# interpolates both the identity and the 3D shape.
python hififace_inference --gpus 0 --model_config config/model.yaml --model_checkpoint_path hififace_opensouce_299999.ckpt --source_image_path assets/inference_samples/01_source.png --target_image_path assets/inference_samples/01_target.png --output_image_path ./01_result_all.gif  --interpolation_all

# interpolates only the identity.
python hififace_inference --gpus 0 --model_config config/model.yaml --model_checkpoint_path hififace_opensouce_299999.ckpt --source_image_path assets/inference_samples/01_source.png --target_image_path assets/inference_samples/01_target.png --output_image_path ./01_result_identity.gif  --interpolation_identity

# interpolates only the 3D shape.
python hififace_inference --gpus 0 --model_config config/model.yaml --model_checkpoint_path hififace_opensouce_299999.ckpt --source_image_path assets/inference_samples/01_source.png --target_image_path assets/inference_samples/01_target.png --output_image_path ./01_result_3d.gif  --interpolation_3d

```
## Our Results
The results from our pre-trained model.

>![](https://raw.githubusercontent.com/maum-ai/hififace/master/assets/obama_trump_biden.gif)
> GIF interpolaiton results from Obama to Trump to Biden back to Obama. The left image interpolates both the identity and the 3D shape. 
> The middle image interpolates only the identity. The right image interpolates only the 3D shape.
> 
![](https://raw.githubusercontent.com/maum-ai/hififace/master/assets/grid_results.png)

## To-Do List
- [ ] Pre-processing Code
- [ ] Colab Notebook

## License

[BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause).

## Implementation Author

[Changho Choi](https://github.com/usingcolor) @ MINDs Lab, Inc. (changho@mindslab.ai)

[Matthew B. Webster](https://github.com/webstah) @ MINDs Lab, Inc. (webster@mindslab.ai)

## Citations
```bibtex
@article{DBLP:journals/corr/abs-2106-09965,
  author    = {Yuhan Wang and
               Xu Chen and
               Junwei Zhu and
               Wenqing Chu and
               Ying Tai and
               Chengjie Wang and
               Jilin Li and
               Yongjian Wu and
               Feiyue Huang and
               Rongrong Ji},
  title     = {HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping},
  journal   = {CoRR},
  volume    = {abs/2106.09965},
  year      = {2021}
}
```