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.
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
:fire: Python code available: https://www.github.com/1adrianb/face-alignment
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.
Note: if you are having troubles compiling thpp (required for fb.python) against the latest version of torch7 please use the version available here: https://github.com/1adrianb/thpp
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
For convenience, a Dockerfile is provided to build images with cuda support and cudnn v5.1:
docker build -t facealignment .
Alternatively you can use the image available on docker hub:
nvidia-docker run -it 1adrianb/facealignment-torch
In order to keep the image small the data and the models are not included and will have to be downloaded separately.
Docker image based on the cuda-torch by kaixhin.
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.