Super-SloMo
PyTorch implementation of "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation" by Jiang et al. [Project] [Paper]
Results
Prerequisites
This codebase was developed and tested with pytorch 0.4.1 and CUDA 9.2.
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
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 |