New Dynamic Sparse Local Patch Transformer (DSLPT) is released at Here.

Sparse Local Patch Transformer

training code is released at Here.

PyTorch evaluation code and pretrained models for SLPT (Sparse Local Patch Transformer).

Installation

Note: this released version was tested on Python3.8, Pytorch 1.10.2 and Windows 11.

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:

     SLPT
     └───Dataset
        │
        └───WFLW
           │
           └───WFLW_annotations
           │   └───list_98pt_rect_attr_train_test
           │   │
           │   └───list_98pt_test
           └───WFLW_images
               └───0--Parade
               │
               └───...
     

2. Download pretrained model from Google Drive.

   • WFLW

   	Model Name	NME (%)	FR0.1 (%)	AUC0.1	download link
   1	SLPT-6-layers	4.143	2.760	0.595	download
   2	SLPT-12-layers	4.128	2.720	0.596	download

   Put the model in ./Weight directory.

3. Test

   python test.py --checkpoint=<model_name>
   For example: python test.py --checkpoint=WFLW_6_layer.pth
   

   Note: if you want to use the model with 12 layers, you need to change _C.TRANSFORMER.NUM_DECODER for 6 to 12 in ./Config/default.py.

Video Demo

We also provide a video demo script.

1. Download face detector, copy the weight yunet_final.pth to ./Weight/Face_Detector/
2. python Camera.py --video_source=<Video Path>

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{SLPT,
  title={Sparse Local Patch Transformer for Robust Face Alignment and Landmarks},
  author={Jiahao Xia and Weiwei Qu and Wenjian Huang and Jianguo Zhang and Xi Wang and Min Xu},
  booktitle={CVPR},
  year={2022}
}

License

SLPT is released under the GPL-2.0 license. Please see the LICENSE file for more information.

Acknowledgments

• This repository borrows or partially modifies the models from HRNet and DETR
• The video demo employs the libfacedetection as the face detector.
• The test videos are provided by DFEW