This is a Pytorch implementation of
Janai, J., Güney, F., Ranjan, A., Black, M. and Geiger, A., Unsupervised Learning of Multi-Frame Optical Flow with Occlusions. ECCV 2018.
[Link to Paper] [Project Page] [Original Torch Code]
- Runs and tested on Pytorch 0.3.1, it should be compatible with higher versions with little/no modifications.
- Correlation package is taken from NVIDIA/flownet2-pytorch and it can be installed using
cd correlation_package
bash make.shIf you are using Pytorch>0.3.1, you can use correlation layer from here.
To use the model, go to your favorite python environment
from back2future import Model
model = Model(pretrained='pretrained/path_to_your_favorite_model')There are two pretrained models in pretrained/, that are fine tuned on Sintel and KITTI in an unsupervised way.
Refer to demo.py for more.
To test performance on KITTI, use
python3 test_back2future.py --pretrained-flow path/to/pretrained/model --kitti-dir path/to/kitti/2015/rootPlease use the [original torch code] for training new models.
This is a reimplementation. License for the original work can be found at JJanai/back2future.
@inproceedings{Janai2018ECCV,
title = {Unsupervised Learning of Multi-Frame Optical Flow with Occlusions },
author = {Janai, Joel and G{"u}ney, Fatma and Ranjan, Anurag and Black, Michael J. and Geiger, Andreas},
booktitle = {European Conference on Computer Vision (ECCV)},
volume = {Lecture Notes in Computer Science, vol 11220},
pages = {713--731},
publisher = {Springer, Cham},
month = sep,
year = {2018},
month_numeric = {9}
}