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Overview of our proposed method. It mainly contains two parts: (a) the off-line generation of multi-scale component dictionaries from large amounts of high-quality images, which have diverse poses and expressions. K-means is adopted to generate K clusters for each component (i.e., left/right eyes, nose and mouth) on different feature scales. (b) The restoration process and dictionary feature transfer (DFT) block that are utilized to provide the reference details in a progressive manner. Here, DFT-i block takes the Scale-i component dictionaries for reference in the same feature level.

(a) Offline generation of multi-scale component dictionaries.

(b) Architecture of our DFDNet for dictionary feature transfer.

Downloading from the following url and put them into ./.

• BaiduNetDisk (s9ht)
• GoogleDrive

These folder structure should be:

.
├── checkpoints                    
│   ├── facefh_dictionary                  
│   │   └── latest_net_G.pth   
├── weights
│   └── vgg19.pth
├── DictionaryCenter512
│   ├── right_eye_256_center.npy
│   ├── right_eye_128_center.npy
│   ├── right_eye_64_center.npy
│   ├── right_eye_32_center.npy
│   └── ...
└── ...

• Pytorch (≥1.1 is recommended)
• dlib
• face-alignment

  cd ./FaceLandmarkDetection
  python setup.py install
  cd ..

1. Crop face from the whole image.

cd ./CropFace
python crop_face_dlib.py

2. Compute the facial landmarks.

cd ./FaceLandmarkDetection
python get_face_landmark.py

3. Run the face restoration.

python test_FaceDict.py

Input	Results

@InProceedings{Li_2020_ECCV,
author = {Li, Xiaoming and Chen, Chaofeng and Zhou, Shangchen and Lin, Xianhui, Zuo, Wangmeng and Zhang, Lei},
title = {Blind Face Restoration via Deep Multi-scale Component Dictionaries},
booktitle = {ECCV},
year = {2020}
}

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.