How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
This is the training code for 2D-FAN and 3D-FAN decribed in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper. Please visit our webpage or read bellow for instructions on how to run the code.
Pretrained models are available on our page.
Note: If you are interested in a binarized version, capable of running on devices with limited resources please also check https://github.com/1adrianb/binary-face-alignment for a demo.
- Clone the github repository and install all the dependencies mentiones above.
git clone https://github.com/1adrianb/face-alignment-training
cd face-alignment-training-
Download the 300W-LP dataset from the authors webpage. In order to train on your own data the dataloader.lua file needs to be adapted.
-
Download the 300W-LP annotations converted to t7 format from here, extract it and move the
landmarksfolder to the root of the 300W-LP dataset.
In order to run the demo please download the required models available bellow and the associated data.
th main.lua -data path_to_300W_LP_datasetIn order to see all the available options please run:
th main.lua --help@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}
}
This pipeline is build around the ImageNet training code avaialable at https://github.com/facebook/fb.resnet.torch and HourGlass(HG) code available at https://github.com/anewell/pose-hg-train