Awesome
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<br/> 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.