/Super-SloMo

PyTorch implementation of Super SloMo by Jiang et al.

Primary LanguagePythonMIT LicenseMIT

Super-SloMo MIT Licence

PyTorch implementation of "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation" by Jiang et al. [Project] [Paper]

Results

Results on UCF101 dataset using the evaluation script provided by 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

Prerequisites

This codebase was developed and tested with pytorch 0.4.1 and CUDA 9.2.

Training

Preparing training data

In order to train the model using the provided code, the data needs to be formatted in a certain manner.
The create_dataset.py script uses ffmpeg to extract frames from videos.
For adobe240fps, download the dataset, unzip it and then run the following command

python data\create_dataset.py --ffmpeg_dir path\to\ffmpeg --videos_folder path\to\adobe240fps\videoFolder --dataset_folder path\to\dataset --dataset adobe240fps

More Info TBA

Evaluation

Pretrained model

You can download the pretrained model trained on adobe240fps dataset here.

More info TBA

To-Do's:

Task Status
Add evaluation script for UCF dataset TBD
Add pretrained model In Progress
Add getting started guide TBD
Add video converter script In progress