Super-SloMo

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.

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: