Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 

README.md

STVUN: Deep Space-Time Video Upsampling Networks

This repository is for STVUN introduced in the following paper

Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, and Seon Joo Kim. "STVUN: Deep Space-Time Video Upsampling Networks", ECCV 2020. PDF, Video

Alt text

Dependencies

Python>=3.6.8, Pytorch=1.2, CUDA version>= 10.2 

Quickstart (Test models)

  1. Clone this github repo

    git clone https://github.com/JaeYeonKang/STVUN-Pytorch
    cd STVUN-PYtorch
    
  2. Compile the correlation package

     cd networks/correlation_package_pytorch
     ./build.sh
    
  3. Place your test dataset in './test' folder (e.g. ./test/Vid4)

  4. Download our pretrained models from link. Then, place the weights in ./pretrained_model

  5. Run demo

    python demo.py --data_dir $DATA_DIR$ \
    --save_dir $SAVE_DIR$ --pre_train $PRETRAINED_MODEL$ \
    --time_step $TIME_STEP$ 
    
    • DATA_DIR : path to test dataset
    • SAVE_DIR : path to save results
    • PRETRAINED_MODEL : path to pretrained model
    • TIME_STEP : number of intermediate frames to generate

    For example,

    python demo.py --data_dir ./test/Vid4 \
    --save_dir ./results --pre_train ./pretrained_model/STVUN.pth \
    --time_step 3 
    

Space-Time Video Testset(STVT) dataset

We collect Space-Time Video Test(STVT) dataset that consists of 12 dynamic scenes with both various motions and spatial details for the joint upsampling evaluation. Each scene has at least 50 frames. You can download our STVT dataset from link

Alt text

Citation

If you use any part of this code in your research, please cite our paper

@article{kang2020deep,
title={Deep Space-Time Video Upsampling Networks},
author={Kang, Jaeyeon and Jo, Younghyun and Oh, Seoung Wug and Vajda, Peter and Kim, Seon Joo},
journal={arXiv preprint arXiv:2004.02432},
year={2020}
}

About

Deep Space-Time Video Upsampling Networks

Resources

Releases

No releases published

Packages

No packages published
You can’t perform that action at this time.