This repository provides code for: Transflow: Transformer as flow learner.
- Clone the repository.
- install dependencies.
pip install -r requirements.txt
-
Download trained model at [GoogleDrive] https://drive.google.com/drive/folders/1XbK0gDshbqZRirEvC9eA4OHB_crn9d0z?usp=sharing), put the demo.pth into
checkpoints/
and put the mae_pvt.pth into theroot/
folder. -
Run the inference
python infer.py --keep_size
- The flow estimations for
demo_frames/
will be saved underdemo_viz_output/demo_frames/
Refer to files with suffix of '_multiframe' for arbitary frame number setting.
python -u train_flow.py --name NAME --stage 'STAGE_NAME' --validation 'VAL_NAME'
If you find the work is useful, please cite it as:
@inproceedings{lu2023transflow,
title={Transflow: Transformer as flow learner},
author={Lu, Yawen and Wang, Qifan and Ma, Siqi and Geng, Tong and Chen, Yingjie Victor and Chen, Huaijin and Liu, Dongfang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={18063--18073},
year={2023}
}