Pytorch implementation of deformable 3D convolution network (D3Dnet). [PDF]

Our code is based on cuda and can perform deformation in any dimension of 3D convolution.

• Python 3
• pytorch (1.2.0), torchvision (0.4.0)
• numpy, PIL
• Visual Studio 2015

Compile deformable 3D convolution:

1. Cd to code/dcn
2. For Windows users, run cmd make.bat For Linux user, run bash make.sh The scripts will build D3D automatically and create some folders.
3. See test.py for example usage.

1. Download the Vimeo dataset and put the images in code/data/Vimeo.
2. Cd to code/data/Vimeo and run generate_LR_Vimeo90K.m to generate training data as below:

  Vimeo
    └── sequences
           ├── 00001
           ├── 00002
           ├── ...
    └── LR_x4
           ├── 00001
           ├── 00002
           ├── ...		
    ├── readme.txt 
    ├── sep_trainlist.txt
    ├── sep_testlist.txt
    └── generate_LR_Vimeo90K.m      

1. Download the dataset Vid4 and SPMC-11 dataset in https://pan.baidu.com/s/1PKZeTo8HVklHU5Pe26qUtw (Code: 4l5r) and put the folder in code/data.
2. (optional) You can also download Vid4 and SPMC-11 or other video datasets and prepare test data in code/data as below:

 data
  └── dataset_1
         └── scene_1
               └── hr    
                  ├── hr_01.png  
                  ├── hr_02.png  
                  ├── ...
                  └── hr_M.png    
               └── lr_x4
                  ├── lr_01.png  
                  ├── lr_02.png  
                  ├── ...
                  └── lr_M.png   
         ├── ...		  
         └── scene_M
  ├── ...    
  └── dataset_N      

Table 1. PSNR/SSIM achieved by different methods.

Table 2. Temporal consistency and computational efficiency achieved by different methods.

We have organized the Matlab code framework of Video Quality Assessment metric T-MOVIE and MOVIE. [Code]
Welcome to have a look and use our code.

Qualitative results achieved by different methods. Blue boxes represent the temporal profiles among different frames.

A demo video is available at https://wyqdatabase.s3-us-west-1.amazonaws.com/D3Dnet.mp4

@article{D3Dnet,
  author = {Ying, Xinyi and Wang, Longguang and Wang, Yingqian and Sheng, Weidong and An, Wei and Guo, Yulan},
  title = {Deformable 3D Convolution for Video Super-Resolution},
  journal = {arXiv preprint arXiv:2004.02803},
  year = {2020},
}

Please contact us at [email protected] for any question.