This is a Pytorch implementation of SRVP. We provide an example using Moving MNIST datasets in this repository.

• April 1, 2025: This paper has been accepted to The 7th IEEE/CVF CVPR Precognition Workshop
• April 10, 2025: Camera-ready submission complete.
• The paper is available at arXiv.

For this example, mnist_test_seq.npy should be in datasets/. You can get mnist_test_seq.npy from Srivastava.

• GPU: NVIDIA RTX A6000 (50GB)
• CUDA 12.4
• Python 3.8
• Pytorch 2.4.1+cu124
• We recommend using Miniconda to set up the virtual environment for training SRVP.
• Install

conda create -n srvp python=3.8
conda activate srvp
pip install -r requirment.txt

• Set up the hyper-parameters in train.sh

ctx=0
epoch=150
bs=8
lr=1e-5
hidden=128
layer=4
mode=train
python main.py --mode=$mode --ctx=$ctx --bs=$bs --epoch=$epoch --lr=$lr --hidden=$hidden --layer=$layer

• Then, run the script file:

bash train.sh

• The trained model, check point files, and a log file will be generated in results/mnist.

• After training, copy train.sh to inference.sh, and revise mode=train to mode=test.

bash inference.sh

• The prediction results and evaluation metrics will be presented in results/mnist. An example of the predicted frames:

• If you want to get the other sample, change the value of idx in main.py. Then, run the inference.sh.

@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Yuseon and Park, Kyongseok},
    title     = {SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
    month     = {June},
    year      = {2025},
    pages     = {5283-5292}
}