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}
}