pytorch-sepconv

News: Check our new CVPR 2018 paper on a faster and higher-quality frame interpolation method.

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
For a third-party fork with video support, consider: https://github.com/dagf2101/pytorch-sepconv

setup

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. In short, the l1 model should be used for quantitative evaluations and the lf model for qualitative comparisons.

python run.py --model lf --first ./images/first.png --second ./images/second.png --out ./out.png

video

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.