This is a reference implementation of the softmax splatting operator, which has been proposed in Softmax Splatting for Video Frame Interpolation [1], using PyTorch. Softmax splatting is a well-motivated approach for differentiable forward warping. It uses a translational invariant importance metric to disambiguate cases where multiple source pixels map to the same target pixel. Should you be making use of our work, please cite our paper [1].
The softmax splatting 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.
The provided example script is using OpenCV to load and display images, as well as to read the provided optical flow file. An easy way to install OpenCV for Python is using the pip install opencv-contrib-python package.
We provide a small script to replicate the third figure of our paper [1]. You can simply run python run.py to obtain the comparison between summation splatting, average splatting, linear splatting, and softmax splatting. Please see this exemplatory run.py for additional information on how to use the provided reference implementation of our proposed softmax splatting operator for differentiable forward warping.
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_CVPR_2020,
author = {Simon Niklaus and Feng Liu},
title = {Softmax Splatting for Video Frame Interpolation},
booktitle = {IEEE International Conference on Computer Vision},
year = {2020}
}
The video above uses materials under a Creative Common license as detailed at the end.