How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
This repository implements a demo of the networks described 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 and access the dataset.
Training code: https://www.github.com/1adrianb/face-alignment-training
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.
- Install the latest Torch7 version (for Windows, please follow the instructions available here)
- Install python 2.7.x
- numpy
- matplotlib
- dlib (required for face detection, if no bbox are provided)
Please note that dlib performs poorly for faces found in challenging poses or difficult lighting conditions and it's provided only as a simple demo. For optimal performance we recommend using other deeplearning based face detection methods.
Clone the github repository and install all the dependencies mentiones above.
git clone https://github.com/1adrianb/2D-and-3D-face-alignment
cd 2D-and-3D-face-alignment
In order to run the demo please download the required models available bellow and the associated data.
th main.lua
In order to see all the available options please run:
th main.lua --help
2D-FAN - trained on 300W-LP and finetuned on iBUG training set.
3D-FAN - trained on 300W-LP
2D-to-3D-FAN - trained on 300W-LP
3D-FAN-depth - trained on 300W-LP
@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}
}
You can download the annotations alongside the images used by visiting our page.