Awesome
The code of 《Fast-Full-frame-Video-Stabilization-with-Iterative-Optimization》
-
Synthetic Dataset
You can run python assets/save_training_video_to_disk.py
to prepare your synthetic dataset. Of course, the corresponding datasets should be prepared firstly (illustrated in the main paper). Then, you should also adjust the directory path in save_training_video_to_disk.py
, including --image_data_path, --csv_path, --save_dir, coco_path
.
-
Generate Confidence Maps
You can run pre_video_flow_process.py
to generate a confidence map sequence for the input video. Before running, please prepare the PDCNet
code and make sure it runs successfully.
-
Core model codes
You can find the code for the network model corresponding to the paper in the core_model
folder, including model.py
, dataset.py
, and loss.py
, etc.
-
Citation
If you find this code helpful, please cite:
@inproceedings{zhao2023fast,
title={Fast full-frame video stabilization with iterative optimization},
author={Zhao, Weiyue and Li, Xin and Peng, Zhan and Luo, Xianrui and Ye, Xinyi and Lu, Hao and Cao, Zhiguo},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={23534--23544},
year={2023}
}