Awesome
STIP (extended from our previous work in CVPR2022)
Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Wen Gao.
Official PyTorch Code for "STIP: A Spatiotemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction"
This work is extended from our previous work STRPM, which has been accepted by CVPR2022. The codes for STRPM have also been made public.
Requirements
- PyTorch 1.7.1
- CUDA 11.0
- CuDNN 8.0.5
- python 3.6.7
Installation
Create conda environment:
$ conda create -n STIP python=3.6.7
$ conda activate STIP
$ pip install -r requirements.txt
$ conda install pytorch==1.7 torchvision cudatoolkit=11.0 -c pytorch
Download repository:
$ git clone git@github.com:ZhengChang467/STIPHR.git
Test on the ucfsports dataset
$ python STIP_run.py --dataset ucfsport
Test on the Human3.6M dataset
$ python STIP_run.py --dataset human36m
Test on the SJTU4K dataset
$ python STIP_run.py --dataset sjtu4k
We plan to share the training soon!
Citation
Please cite the following paper if you feel this repository useful.
@article{chang2022strpm,
title={STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction},
author={Chang, Zheng and Zhang, Xinfeng and Wang, Shanshe and Ma, Siwei and Gao, Wen},
journal={arXiv preprint arXiv:2203.16084},
year={2022}
}
License
See MIT License