FuxiCV/MeInGame

MeInGame: Create a Game Character Face from a Single Portrait, AAAI 2021

MeInGame: Create a Game Character Face from a Single Portrait

This is the official PyTorch implementation of the AAAI 2021 paper: J. Lin, Y. Yuan, and Z. Zou, MeInGame: Create a Game Character Face from a Single Portrait, the Association for the Advance of Artificial Intelligence (AAAI), 2021.

3D display of the created game characters (click to view):

Getting Started

Requirements

• The code runs on both Windows and Linux.
• For compatibility reasons, we use CUDA 10.0, PyTorch 1.4 and Tensorflow 1.14.
• Basel Face Model 2009 (BFM09).
• Expression Basis (transferred from Facewarehouse by Guo et al.).
• A modified version of PyTorch3D. Since there is a bug of boardcasting in the official version, we use our modified version.

Install Dependencies

pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
pip install opencv-python fvcore h5py scipy scikit-image dlib face-alignment scikit-learn tensorflow-gpu==1.14.0 gast==0.2.2
pip install "git+https://github.com/Agent-INF/pytorch3d.git@3dface"

Testing with pre-trained network

1. Clone the repository

git clone https://github.com/FuxiCV/MeInGame
cd MeInGame

2. Prepare the Basel Face Model following thet instructions on Deep3DFaceReconstruction, and rename those files as follows: Deep3DFaceReconstruction/BFM/BFM_model_front.mat -> ./data/models/bfm2009_face.mat Deep3DFaceReconstruction/BFM/similarity_Lm3D_all.mat -> ./data/models/similarity_Lm3D_all.mat Deep3DFaceReconstruction/network/FaceReconModel.pb -> ./data/models/FaceReconModel.pb

3. Download the pre-trained model, put the .pth file into ./checkpoints/celeba_hq_demo subfolder, and the .pkl file into ./data/models subfolder.

4. Run the code.

python main.py -m test -i demo
# Or
python main.py -m test -i demo -c
# it will run on the CPU, if you don't have a qualified GPU.

5. ./data/test subfolder contains several test images and ./results subfolder stores their reconstruction results. For each input test image, serveral output files can be obtained after running the demo code:

• “xxx_input.jpg”: an RGB image after alignment, which is the input to the network
• “xxx_neu.obj”: the reconstructed 3D face in neutral expression, which can be viewed in MeshLab.
• “xxx_uv.png”: the uvmap corresponding to the obj file.

Training with CelebA-HQ dataset

Data preparation

1. Run following command to create training dataset from in-the-wild images.

python create_dataset.py
# You can modify the input_dir to your input images directory.

2. Download our RGB 3D face dataset (TBA), unzip it, and place it into the ./data/dataset/celeba_hq_gt subfolder.

Training networks

After the dataset is ready, you can train the network with the following command:

python main.py -m train

Citation

Please cite the following paper if this model helps your research:

@inproceedings{lin2021meingame,
    title={MeInGame: Create a Game Character Face from a Single Portrait},
    author={Lin, Jiangke and Yuan, Yi and Zou, Zhengxia},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    year={2021}
}