Welcome to the fascinating world of video prediction! This blog post guides you through the setup and usage of the Dynamic Multi-Scale Voxel Flow Network (DMVFN), a state-of-the-art model for generating future frames in videos. This project was accepted at **CVPR2023**, a highlight featuring only 10% of the papers. Let’s dive in!

Project Overview

The DMVFN is designed to facilitate video prediction tasks by leveraging a multi-scale voxel flow network. Essentially, it predicts what happens next in a video by training on past frames. Imagine you are sitting down to watch a movie; your brain naturally starts predicting what will happen next based on the context of the scenes you’ve already seen. This model uses similar techniques, applying advanced algorithms to achieve precision in predictions.

How to Get Started

1. Installation

• Open your terminal.
• Clone the repository using the command:

git clone https://github.com/megvii-research/CVPR2023-DMVFN.git

• Navigate to the directory:

cd CVPR2023-DMVFN

• Install the required packages:

pip3 install -r requirements.txt

• Download the pretrained models from Google Drive, and move the pretrained parameters to CVPR2023-DMVFN/pretrained_models.

2. Data Preparation

Organizing your data is critical as the DMVFN model feeds on structured datasets. Here’s how to set it up:

• For Cityscapes, download the dataset from here.
• For KITTI, register and download the dataset from here.
• For UCF101 and Vimeo, you can download them as per instructions provided.

3. Running the Model

Training

To train your model, use the following commands based on the dataset:

• For Cityscapes:

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

• For KITTI:

python3 -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 ./scripts/train.py --train_dataset KittiTrainDataset --val_datasets KittiValDataset --batch_size 8 --num_gpu 8

• For UCF101 and Vimeo datasets:

python3 -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 ./scripts/train.py --train_dataset UCF101TrainDataset --val_datasets DavisValDataset VimeoValDataset --batch_size 8 --num_gpu 8

Testing

To generate test results, run:

python3 ./scripts/test.py --val_datasets CityValDataset [optional: KittiValDataset, DavisValDataset, VimeoValDataset] --load_path path_of_pretrained_weights --save_image

Troubleshooting

Here are some common issues you might encounter and their solutions:

• Ensure all dataset paths are correct to avoid file not found errors.
• If you face memory issues, try reducing the batch size.
• For installation issues, double-check that all required libraries from requirements.txt are correctly installed.
• For performance problems, check your GPU settings and ensure that enough resources are allocated.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This model is a powerful tool for video prediction, pushing the frontiers of computer vision. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.