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AdaptiveWingLoss

arXiv

Pytorch Implementation of Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression.

Update Logs:

October 28, 2019

• Pretrained Model and evaluation code on WFLW dataset is released.

Installation

Note: Code was originally developed under Python2.X and Pytorch 0.4. This released version was revisioned from original code and was tested on Python3.5.7 and Pytorch 1.3.0.

Install system requirements:

sudo apt-get install python3-dev python3-pip python3-tk libglib2.0-0

Install python dependencies:

pip3 install -r requirements.txt

Run Evaluation on WFLW dataset

1. Download and process WFLW dataset

   • Download WFLW dataset and annotation from Here.
   • Unzip WFLW dataset and annotations and move files into ./dataset directory. Your directory should look like this:

     AdaptiveWingLoss
     └───dataset
        │
        └───WFLW_annotations
        │   └───list_98pt_rect_attr_train_test
        │   │
        │   └───list_98pt_test
        │
        └───WFLW_images
            └───0--Parade
            │
            └───...
     

   • Inside ./dataset directory, run:

     python convert_WFLW.py
     

     A new directory ./dataset/WFLW_test should be generated with 2500 processed testing images and corresponding landmarks.

2. Download pretrained model from Google Drive and put it in ./ckpt directory.

3. Within ./Scripts directory, run following command:

   sh eval_wflw.sh
   

   <img src='images/wflw_table.png' width="800px"> *GTBbox indicates the ground truth landmarks are used as bounding box to crop faces.

Future Plans

• Release evaluation code and pretrained model on WFLW dataset.

• Release training code on WFLW dataset.

• Release pretrained model and code on 300W, AFLW and COFW dataset.

• Replease facial landmark detection API

Citation

If you find this useful for your research, please cite the following paper.

@InProceedings{Wang_2019_ICCV,
author = {Wang, Xinyao and Bo, Liefeng and Fuxin, Li},
title = {Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}

Acknowledgments

This repository borrows or partially modifies hourglass model and data processing code from face alignment and pose-hg-train.