Simply download Colab-Super-SloMo.ipynb and open it inside your Google Drive or click here and copy the file with "File > Save a copy to Drive..." into your Google Drive.
PyTorch implementation of "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation" by Jiang H., Sun D., Jampani V., Yang M., Learned-Miller E. and Kautz J. [Project] [Paper]
Check out our paper "Deep Slow Motion Video Reconstruction with Hybrid Imaging System" published in TPAMI.
- If you can't open
Colab-Super-SloMo.ipynbinside your Google Drive, try this colab link and save it to your Google Drive. The "open in Colab"-button can be missing in Google Drive, if that person never used Colab. - Google Colab does assign a random GPU. It depends on luck.
- The Google Colab VM does have a maximum session length of 12 hours. Additionally there is a 30 minute timeout if you leave colab. The VM will be deleted after these timeouts.
Results on UCF101 dataset using the evaluation script provided by paper's author. The get_results_bug_fixed.sh script was used. It uses motions masks when calculating PSNR, SSIM and IE.
| Method | PSNR | SSIM | IE |
|---|---|---|---|
| DVF | 29.37 | 0.861 | 16.37 |
| SepConv - L_1 | 30.18 | 0.875 | 15.54 |
| SepConv - L_F | 30.03 | 0.869 | 15.78 |
| SuperSloMo_Adobe240fps | 29.80 | 0.870 | 15.68 |
| pretrained mine | 29.77 | 0.874 | 15.58 |
| SuperSloMo | 30.22 | 0.880 | 15.18 |
This codebase was developed and tested with pytorch 0.4.1 and CUDA 9.2 and Python 3.6. Install:
For GPU, run
conda install pytorch=0.4.1 cuda92 torchvision==0.2.0 -c pytorchFor CPU, run
conda install pytorch-cpu=0.4.1 torchvision-cpu==0.2.0 cpuonly -c pytorch- TensorboardX for training visualization
- tensorflow for tensorboard
- matplotlib for training graph in notebook.
- tqdm for progress bar in video_to_slomo.py
- numpy
You can download the pretrained model trained on adobe240fps dataset here.
Parts of the code is based on TheFairBear/Super-SlowMo