Implementation of "Quadratic video interpolation", NeurIPS 2019.
The following pakages are required to run the code:
- python==3.8
- pytorch==1.5.1
- cudatoolkit==10.1
- torchvision==0.6.1
- cupy==8.6.0
- tensorboardX
- opencv-python
- easydict
- Download pretrained model and put it in "./qvi_release"
- Download weights of PWC-Net and put it in "./utils"
- Put your sequence in "datasets/example" with structure as
seq1
--00000.png
--00001.png
--...
seq2
--00000.png
--00001.png
--...
- Then run the demo:
python demo.py configs/test_config.py
The output will be in "outputs/example". Note that all settings are in config files under the folder "./configs".
- Download the QVI-960 dataset for training and put it in the folder "datasets"
- Download the validation data which is a subset of the Adobe-240 dataset, and put it in the folder "datasets"
- Then run the training code:
python train.py configs/train_config.py
More datasets for evaluation:
You can use "datas/Sequence.py" to conveniently load the test datasets.
Please consider citing this paper if you find the code and data useful in your research:
@inproceedings{qvi_nips19,
title={Quadratic video interpolation},
author={Xiangyu Xu and Li Siyao and Wenxiu Sun and Qian Yin and Ming-Hsuan Yang},
booktitle = {NeurIPS},
year={2019}
}