SelFlow: Self-Supervised Learning of Optical Flow
The official Tensorflow implementation of SelFlow (CVPR 2019 Oral).
Authors: Pengpeng liu, Michael R. Lyu, Irwin King, Jia Xu
Our SelFlow is the 1st place winner on Sintel Optical Flow Benchmark from November 2018 to November 2019.
There is a dockerfile with the neccesary dependencies which you can build with the command below.
docker build --network=host -t selflow .
You can run the docker image with command below.
docker run -it --rm --network=host -w /SelFlow selflow
You can then follow the instructions below to test the model
By default, you can get the testing results using the pre-trained Sintel model by running:
python main.py
Both forward and backward optical flow and their visualization will be written to the output folder.
Please refer to the configuration file template config for a detailed description of the different operating modes.
Check models for our pre-trained models on different datasets.
If you find SelFlow useful in your research, please consider citing:
@inproceedings{Liu:2019:SelFlow,
title = {SelFlow: Self-Supervised Learning of Optical Flow},
author = {Pengpeng Liu and Michael R. Lyu and Irwin King and Jia Xu},
booktitle = {CVPR},
year = {2019}
}
@inproceedings{Liu:2019:DDFlow,
title = {DDFlow: Learning Optical Flow with Unlabeled Data Distillation},
author = {Pengpeng Liu and Irwin King and Michael R. Lyu and Jia Xu},
booktitle = {AAAI},
year = {2019}}
Part of our codes are adapted from PWC-Net and UnFlow, we thank the authors for their contributions.