LUVLi and UGLLI Face Alignment

The code is officially available here

LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood, CVPR 2020

[slides], [1min_talk], [supp],[demo]

UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss, ICCV Workshops on Statistical Deep Learning in Computer Vision 2019

[slides], [poster], [news], [Best Oral Presentation Award]

References

Please cite the following papers if you find this repository useful:

@inproceedings{kumar2020luvli,
  title={LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood},
  author={Kumar, Abhinav and Marks, Tim K. and Mou, Wenxuan and Wang, Ye and Jones, Michael and Cherian, Anoop and Koike-Akino, Toshiaki and Liu, Xiaoming and Feng, Chen},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

@inproceedings{kumar2019uglli,
  title={UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss},
  author={Kumar, Abhinav and Marks, Tim K and Mou, Wenxuan and Feng, Chen and Liu, Xiaoming},
  booktitle={ICCV Workshops on Statistical Deep Learning in Computer Vision},
  year={2019}
}

Evaluation of our pre-trained models

Copy the pre-trained models to the abhinav_model_dir first. The directory structure should look like this:

./FaceAlignmentUncertainty/
|--- abhinav_model_dir/
|           |--- run_108
|           |       |--lr-0.00002-49.pth.tar
|           |
|           |--- run_109
|           |       |--lr-0.00002-49.pth.tar
|           |
|           |--- run_507
|           |       |--lr-0.00002-49.pth.tar
|           |
|           |--- run_1005
|           |       |--lr-0.00002-49.pth.tar
|           |
|           |--- run_5004
|           |       |--lr-0.00002-49.pth.tar
|  ...

Next type the following:

./scripts_evaluation.sh

In case you want to get our qualitative plots and also the transformed figures, type:

python plot/show_300W_images_overlaid_with_uncertainties.py --exp_id abhinav_model_dir/run_109_evaluate/ --laplacian
python plot/plot_uncertainties_in_transformed_space.py          -i run_109_evaluate/300W_test --laplacian
python plot/plot_residual_covariance_vs_predicted_covariance.py -i run_109_evaluate --laplacian
python plot/plot_histogram_smallest_eigen_value.py              -i run_109_evaluate --laplacian