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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.

Requirements

Dockerfile

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

Usage

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.

Testing

Pre-trained Models

Check models for our pre-trained models on different datasets.

Citation

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}}

Acknowledgement

Part of our codes are adapted from PWC-Net and UnFlow, we thank the authors for their contributions.