This project is updated from dagf2101/pytorch-sepconv
And the original author is sniklaus/pytorch-sepconv
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some code robustness
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the code is updated with cupy library with author's newest version
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increase a progress bar when processing
using moviepy as video process library:
pip install moviepy
using cupy :
pip install cupy
or pip install cupy-cudaxx
where xx is your cuda version
Below is the origin 'readme'
This is a reference implementation of Video Frame Interpolation via Adaptive Separable Convolution [1] using PyTorch. Given two frames, it will make use of adaptive convolution [2] in a separable manner to interpolate the intermediate frame. Should you be making use of our work, please cite our paper [1].
For the Torch version of this work, please see: https://github.com/sniklaus/torch-sepconv
To download the pre-trained models, run bash download.bash
.
The separable convolution layer is implemented in CUDA using CuPy, which is why CuPy is a required dependency. It can be installed using pip install cupy
or alternatively using one of the provided binary packages as outlined in the CuPy repository.
usage
To run it on your own pair of frames, use the following command. You can either select the l1
or the lf
model, please see our paper for more details.
Image:
python run.py --model lf --first ./images/first.png --second ./images/second.png --out ./result.png
Video:
python run.py --model lf --video ./video.mp4 --video-out ./result.mp4
The provided implementation is strictly for academic purposes only. Should you be interested in using our technology for any commercial use, please feel free to contact us.
[1] @inproceedings{Niklaus_ICCV_2017,
author = {Simon Niklaus and Long Mai and Feng Liu},
title = {Video Frame Interpolation via Adaptive Separable Convolution},
booktitle = {IEEE International Conference on Computer Vision},
year = {2017}
}
[2] @inproceedings{Niklaus_CVPR_2017,
author = {Simon Niklaus and Long Mai and Feng Liu},
title = {Video Frame Interpolation via Adaptive Convolution},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2017}
}
This work was supported by NSF IIS-1321119. The video above uses materials under a Creative Common license or with the owner's permission, as detailed at the end.