CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow

This repository contains the source code for our optical flow estimation method:

CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow
Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, Hongyuan Zhu

Environments

You will have to choose cudatoolkit version to match your compute environment. The code is tested on PyTorch 1.8.0 but other versions may also work.

pip install -r requirements.txt

Train

sh train-craft-f2full.sh

Evaluate

sh evaluate-craft-f2full.sh

License

WTFPL. See LICENSE file.

Citation

@InProceedings{craft, author="Sui, Xiuchao and Li, Shaohua and Geng, Xue and Wu, Yan and Xu, Xinxing and Liu, Yong and Goh, Rick Siow Mong and Zhu, Hongyuan",
title="CRAFT: Cross-Attentional Flow Transformers for Robust Optical Flow",
booktitle="CVPR",
year="2022"}

Acknowledgement

The overall code framework is adapted from GMA. We thank the authors for their contributions.