face-alignment-pytorch

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• 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 [[https://github.com/1adrianb/face-alignment-training][face-alignment-training]]. Please visit [[https://www.adrianbulat.com][author's webpage]] or [[https://arxiv.org/abs/1703.07332][arxiv]] for technical details.

  This is a reinplement of training and Inference code for 2D-FAN and 3D-FAN decribed in "How far" paper. And up to now, since I have seeked for a pytorch version for a long time but always find some error in others version, maybe this job is the only one with both train code and inference code in pytorch. And I also have checked the results. This code take some other codes in github for reference, such as [[https://github.com/hzh8311/pyhowfar][pyhowfar]], [[https://github.com/1adrianb/face-alignment][face-alignment]].

  Pretrained models are available soon.

** Requirments

• Install the latest [[http://pytorch.org][PyTorch]], version 0.4.1 is fully supported and there is no further test on older version.
• Install python 3.6.6, which is fully supported and there is no further test on older version.

*** Packages

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

** Setup

1. Clone the github repository and install all the dependencies mentiones above.

#+BEGIN_SRC bash git clone https://github.com/GuohongLi/face-alignment-pytorch.git cd face-alignment-pytorch #+END_SRC

2. Download the 300W-LP dataset from the [[http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/Database/300W-LP/main.htm][Here]].

3. Download the 300W-LP annotations converted to t7 format by paper author from [[https://www.adrianbulat.com/downloads/FaceAlignment/landmarks.zip][here]], extract it and move the landmarks folder to the root of the 300W-LP dataset.

4. Download the face detector pretrain model file from [[https://www.adrianbulat.com/downloads/python-fan/s3fd_convert.pth][s3fd_convert.pth]]

** Usage

In order to run the demo please download the required models available bellow and the associated data.

*** Train

#+BEGIN_SRC bash python train.py #+END_SRC

In order to see all the available options please run:

#+BEGIN_SRC bash python train.py --help #+END_SRC

*** Inference

#+BEGIN_SRC bash python inference.py #+END_SRC

In order to see all the available options please run:

#+BEGIN_SRC bash python inference.py --help #+END_SRC

** What's different?

• Pythoner friendly and there is no need for .t7 format annotations
• Add 300-W-LP test set for validation.
• Followed the excatly same training procedure described in the paper (except binary network part).
• Add model evaluation in terms of Mean error, [email protected]
• TODO: add evaluation on test sets (300W, 300VW, AFLW2000-3D etc.).

** Citation

#+BEGIN_SRC bibtex @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} } #+END_SRC