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SimVP: Simpler yet Better Video Prediction

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In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.

The pre-trained models and benchmarks will be available in SimVPv2.

SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!

This repository contains the implementation code for paper:

SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.

Introduction


From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.

Dependencies

  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm

Overview

  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.

Install

This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:

 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset

 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset

We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.

KTH dataset

We provide a Dropbox to download the KTH dataset.

Citation

If you are interested in our repository and our paper, please cite the following paper:

@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact

If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!

About

The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.

Resources

Readme
Activity
Custom properties

Stars

301 stars

Watchers

3 watching

Forks

49 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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SimVP: Simpler yet Better Video Prediction
GitHub starsGitHub forks
In the example, the default epoch is 50. Please read our paper, and train 1000~2000 epochs for repruducing this work! I will not respond to such a lowly mistake.
The pre-trained models and benchmarks will be available in SimVPv2.
SimVPv2 is available on https://github.com/chengtan9907/SimVPv2, which performs better than SimVP (15.05 MSE on Moving MNIST) and is in the review process. If our work is helpful for your research, we would hope you give us a star and citation. Thanks!
This repository contains the implementation code for paper:
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li. In CVPR, 2022.
Introduction

From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies. We admire these progresses but are confused about the necessity: is there a simple method that can perform comparably well? This paper proposes SimVP, a simple video prediction model that is completely built upon CNN and trained by MSE loss in an end-to-end fashion. Without introducing any additional tricks and complicated strategies, we can achieve state-of-the-art performance on five benchmark datasets. Through extended experiments, we demonstrate that SimVP has strong generalization and extensibility on real-world datasets. The significant reduction of training cost makes it easier to scale to complex scenarios. We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.
Dependencies
  • torch
  • scikit-image=0.16.2
  • numpy
  • argparse
  • tqdm
Overview
  • API/ contains dataloaders and metrics.
  • main.py is the executable python file with possible arguments.
  • model.py contains the SimVP model.
  • exp.py is the core file for training, validating, and testing pipelines.
Install
This project has provided an environment setting file of conda, users can easily reproduce the environment by the following commands:
 conda env create -f environment.yml
conda activate SimVP

Moving MNIST dataset
 cd ./data/moving_mnist
bash download_mmnist.sh

TaxiBJ dataset
We provide a Dropbox to download TaxiBJ dataset. Users can download this dataset and put it into ./data/taxibj.
KTH dataset
We provide a Dropbox to download the KTH dataset.
Citation
If you are interested in our repository and our paper, please cite the following paper:
@InProceedings{Gao_2022_CVPR,
author = {Gao, Zhangyang and Tan, Cheng and Wu, Lirong and Li, Stan Z.},
title = {SimVP: Simpler Yet Better Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3170-3180}
}

Contact
If you have any questions, feel free to contact us through email (tancheng@westlake.edu.cn, gaozhangyang@westlake.edu.cn). Enjoy!
About
The official implementation of the CVPR'22 paper SimVP: Simpler Yet Better Video Prediction.
Resources
Readme
Activity
Custom properties
Stars
301 stars
Watchers
3 watching
Forks
49 forks
Report repository
Releases
Packages
Contributors
Languages