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an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch
| Date | Stars |
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| 2026-07-24 | 1021 |
| 2026-07-25 | 1021 |
| 2026-07-28 | 1021 |
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| 2026-08-06 | 1021 |
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This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv
# sepconv-slomo
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](http://sniklaus.com/papers/adaconv) [2] in a separable manner to interpolate the intermediate frame. Should you be making use of our work, please cite our paper [1].
<a href="https://arxiv.org/abs/1708.01692"><img src="http://content.sniklaus.com/sepconv/paper.jpg" alt="Paper" width="100%"></a>
For a reimplemntation of our work, see: https://github.com/martkartasev/sepconv
<br />
And for another adaptation, consider: https://github.com/HyeongminLEE/pytorch-sepconv
<br />
For softmax splatting, please see: https://github.com/sniklaus/softmax-splatting
## setup
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](https://docs.cupy.dev/en/stable/install.html#installing-cupy) as outlined in the CuPy repository.
If you plan to process videos, then please also make sure to have `pip install moviepy` installed.
## 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. In short, the `l1` model should be used for quantitative evaluations and the `lf` model for qualitative comparisons.
```
python run.py --model lf --one ./images/one.png --two ./images/two.png --out ./out.png
```
To run in on a video, use the following command.
```
python run.py --model lf --video ./videos/car-turn.mp4 --out ./out.mp4
```
For a quick benchmark using examples from the Middlebury benchmark for optical flow, run `python benchmark.py`. You can use it to easily verify that the provided implementation runs as expected.
## video
<a href="http://content.sniklaus.com/sepconv/video.mp4"><img src="http://content.sniklaus.com/sepconv/video.jpg" alt="Video" width="100%"></a>
## license
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.
## references
```
[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}
}
```
## acknowledgment
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.Excerpt of 3,087 characters
Read on GitHub
Simon Niklaus · Canada
61
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8443ac98d5492cdd, topic:deep-learning, topic:pytorch
matched fp:8443ac98d5492cdd, topic:cuda