A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
Project Overview
The project "A Dynamic Multi-Scale Voxel Flow Network for Video Prediction," also known as DMVFN, is a cutting-edge development in the field of video prediction. This project has received high recognition by being featured at the esteemed conference CVPR2023, where it was highlighted as one of the top papers, making up the top 10% of accepted works. The central focus of DMVFN is to advance video prediction technology through a novel approach.
Key Highlights
- Homepage and Resources: Interested individuals can view the project's homepage, access a Colab demo, read the academic paper on arXiv, watch a YouTube presentation, and even view a poster presentation.
- Advanced Model: DMVFN proposes a state-of-the-art model that significantly enhances the accuracy and efficiency of predicting future frames in videos.
Usage Instructions
Installation
To get started, users need to clone the project repository from GitHub. After navigating into the project directory, the necessary Python packages can be installed using the provided requirements file.
git clone https://github.com/megvii-research/CVPR2023-DMVFN.git
cd CVPR2023-DMVFN
pip3 install -r requirements.txt
Pre-trained models can be downloaded from Google Drive, or alternatively from 百度网盘 for those in regions with access.
Data Preparation
The project requires datasets from several sources, including Cityscapes, KITTI, UCF101, and Vimeo. Each dataset requires specific steps to prepare them for training and testing within DMVFN.
- Cityscapes: Download and unzip the provided dataset, then prepare it using the included script.
- KITTI: Obtain the dataset via Google Drive, organize the files properly, and prepare them using another provided script.
- UCF101 and Vimeo: These datasets can be acquired and processed similarly through downloading and unzipping.
Running the Project
Training
Depending on the dataset, users can initiate training using the specified commands for Cityscapes, KITTI, or other datasets. As an example, the command for Cityscapes is:
python3 -m torch.distributed.launch --nproc_per_node=8 \
--master_port=4321 ./scripts/train.py \
--train_dataset CityTrainDataset \
--val_datasets CityValDataset \
--batch_size 8 \
--num_gpu 8
Testing
For testing the model, there are options to download pre-prepared test splits for each dataset. Users can generate test results using the accompanying script, specifying which dataset to validate against.
python3 ./scripts/test.py \
--val_datasets CityValDataset \
--load_path path_of_pretrained_weights \
--save_image
Single Image Prediction
For a more hands-on approach, the project provides a script for predicting the next video frame from two given prior frames. The command for this simple test is:
python3 ./scripts/single_test.py \
--image_0_path ./images/sample_img_0.png \
--image_1_path ./images/sample_img_1.png \
--load_path path_of_pretrained_weights \
--output_dir pred.png
Importance and Contribution
The DMVFN project represents a significant advancement in video prediction, offering not just enhancements in prediction quality but also potential applications in various fields such as video compression and intelligent surveillance systems. By sharing their model and findings, the researchers invite others to build upon their work, fostering further innovation.
Citation and Further Reading
The project team suggests referencing their paper when using or building upon their model:
@inproceedings{hu2023dmvfn,
title={A Dynamic Multi-Scale Voxel Flow Network for Video Prediction},
author={Hu, Xiaotao and Huang, Zhewei and Huang, Ailin and Xu, Jun and Zhou, Shuchang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
For more insights into related technologies, they recommend exploring other works in video frame interpolation and optimization techniques for video prediction.