FAN: A Python repository from nickuntitled

Pytorch version of 'How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)'

For official torch7 version please refer to face-alignment-training [https://github.com/1adrianb/face-alignment-training]

This is a reinplement of training code for 2D-FAN and 3D-FAN decribed in "How far" paper. Please visit author's webpage [https://www.adrianbulat.com] or arxiv [https://arxiv.org/abs/1703.07332] for technical details.

Thanks for bearpaw's excellent work on human pose estimation [https://github.com/bearpaw/pytorch-pose] . And in this project, I reused a branch of helper function from pytorch-pose.

Pretrained models are available soon.

Requirments

Install the latest Pytorch [http://pytorch.org], version 1.0.0.

Packages

[https://www.scipy.org/][scipy]
[https://pytorch.org][torchvision]
[https://pypi.python.org/pypi/progress] progress (optional) for better visualization.

Train

Clone the github repository and install all the dependencies mentiones above.
```
  git clone https://github.com/lippman1125/pytorch_FAN
```
Download the LS3D-W dataset from the authors webpage (https://www.adrianbulat.com/face-alignment).
Download the 300W-LP annotations converted to t7 format by paper author from (https://www.adrianbulat.com/downloads/FaceAlignment/landmarks.zip).

We merge LS3D-W dataset and 300W-LP dataset together to train our model.

cd data/LS3D-W
tree -d -L 1

  |-- 300VW-3D                
  |-- 300W-Testset-3D         
  |-- 300W_LP                 
  |-- AFLW2000-3D-Reannotated 
  `-- Menpo-3D

Validation set is testset of 300W-LP

Start to train:
```
  ./exp/train.sh
```

Test

Run the demo.
```
  python demo_video.py gpu
```

What's different?

Train FAN only includes 2 Hourglass
Total Params: 11.55M

Performance

Citation

  {
   @inproceedings{bulat2017far,
     title={How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)},
     author={Bulat, Adrian and Tzimiropoulos, Georgios},
     booktitle={International Conference on Computer Vision},
     year={2017}
   }

Refenerce

https://github.com/hzh8311/pyhowfar

nickuntitled/FAN