HongwenZhang / JVCR-3Dlandmark

[TIP 2019] Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild

Joint Voxel and Coordinate Regression (JVCR) for 3D Facial Landmark Localization

This repository includes the PyTorch code of the JVCR method described in Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild (IEEE Transactions on Image Processing, 2019).

Requirements

• python 2.7

packages

• PyTorch
• NumPy
• Matplotlib
• progress

Usage

Clone the repository and install the dependencies mentioned above

git clone https://github.com/HongwenZhang/JVCR-3Dlandmark.git
cd JVCR-3Dlandmark

Then, you can run the demo code or train a model from stratch.

Demo

1. Download the pre-trained model (trained on 300W-LP) and put it into the checkpoint directory

2. Run the demo code

python run_demo.py --verbose

Training

1. Prepare the training and evaluation datasets

• Download 300W-LP and AFLW3000-3D
• Create soft links to the dataset directories

ln -s /path/to/your/300W_LP data/300wLP/images
ln -s /path/to/your/aflw2000 data/aflw2000/images

• Download .json annotation files from here and put them into data/300wLP and data/aflw2000 respectively

2. Run the training code

python train.py --gpus 0 -j 4

Acknowledgment

The code is developed upon PyTorch-Pose. Thanks to the original author.

Citation

If the code is helpful in your research, please cite the following paper.

@article{zhang2019adversarial,
  title={Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild},
  author={Zhang, Hongwen and Li, Qi and Sun, Zhenan},
  journal={IEEE Transactions on Image Processing},
  volume={28},
  number={9},
  pages={4526--4540},
  year={2019},
  publisher={IEEE}
}