Official repository for the CVPR 2023 paper, "Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields"
[Paper] [Supp] [Oral(YouTube)]
Dataset of high-resolution (1440x975), high-fps (170fps) video frames plus high resolution events with extremely large motion using the beam-splitter acquisition system:
You can download the raw-data(collected frame and events) from this links
** Cautions: the x,y coordinates of the raw event file are multiplied by 128.
- PyTorch 1.8.0
- CUDA 11.2
- python 3.8
Download repository:
$ git clone https://github.com/intelpro/CBMNet
Install correlation package:
$ sh install_correlation.sh
Download network weights(trained on ERF-X170FPS datasets) and place downloaded model in ./pretrained_model/
- [Ours]
- [Ours-Large]
Generate an intermediate video frame using ours model:
$ python run_samples.py --model_name ours --ckpt_path pretrained_model/ours_weight.pth --save_output_dir ./output --image_number 0
Also, you can generate intermediate video frame using ours-large model:
$ python run_samples.py --model_name ours_large --ckpt_path pretrained_model/ours_large_weight.pth --save_output_dir ./output --image_number 0
The model pretrained on the BS-ERGB dataset can be downloaded from the following link:
Taewoo Kim, Yujeong Chae, Hyun-kyurl Jang, and Kuk-Jin Yoon" Event-based Video Frame Interpolation with Cross-modal Asymmetric Bidirectional Motion Fields", In CVPR, 2023.
@InProceedings{Kim_2023_CVPR,
author = {Kim, Taewoo and Chae, Yujeong and Jang, Hyun-Kurl and Yoon, Kuk-Jin},
title = {Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {18032-18042}
}
If you have any question, please send an email to taewoo([email protected])
The project codes and datasets can be used for research and education only.