MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

Related tags

Deep Learning MAU
Overview

MAU (NeurIPS2021)

Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo.

Official PyTorch Code for "MAU: A Motion-Aware Unit for Video Prediction and Beyond" [paper]

Requirements

  • PyTorch 1.7
  • CUDA 11.0
  • CuDNN 8.0.5
  • python 3.6.7

Installation

Create conda environment:

    $ conda create -n MAU python=3.6.7
    $ conda activate MAU
    $ pip install -r requirements.txt
    $ conda install pytorch==1.7 torchvision cudatoolkit=11.0 -c pytorch

Download repository:

    $ git clone [email protected]:ZhengChang467/MAU.git

Unzip MovingMNIST Dataset:

    $ cd data
    $ unzip mnist_dataset.zip

Test

    $ python MAU_run.py --is_train False

Train

    $ python MAU_run.py --is_train True

We plan to share the train codes for other datasets soon!

Citation

Please cite the following paper if you feel this repository useful.

@article{chang2021mau,
title={MAU: A Motion-Aware Unit for Video Prediction and Beyond},
author={Chang, Zheng and Zhang, Xinfeng and Wang, Shanshe and Ma, Siwei and Ye, Yan and Xinguang, Xiang and Gao, Wen},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}}

License

See MIT License

You might also like...
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021)
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021)

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) Overview Prerequisites Linux Pytho

[NeurIPS2021] Code Release of K-Net: Towards Unified Image Segmentation

K-Net: Towards Unified Image Segmentation Introduction This is an official release of the paper K-Net:Towards Unified Image Segmentation. K-Net will a

PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)
PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Lip to Speech Synthesis with Visual Context Attentional GAN This repository contains the PyTorch implementation of the following paper: Lip to Speech

SLAMP: Stochastic Latent Appearance and Motion Prediction

SLAMP: Stochastic Latent Appearance and Motion Prediction Official implementation of the paper SLAMP: Stochastic Latent Appearance and Motion Predicti

[ICCV'21] Official implementation for the paper  Social NCE: Contrastive Learning of Socially-aware Motion Representations
[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

CrowdNav with Social-NCE This is an official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations by

Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th place solution

Lyft Motion Prediction for Autonomous Vehicles Code for the 4th place solution of Lyft Motion Prediction for Autonomous Vehicles on Kaggle. Discussion

[arXiv] What-If Motion Prediction for Autonomous Driving β“πŸš—πŸ’¨
[arXiv] What-If Motion Prediction for Autonomous Driving β“πŸš—πŸ’¨

WIMP - What If Motion Predictor Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations] Setup Requirements The W

 Waymo motion prediction challenge 2021: 3rd place solution
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution πŸ“œ Technical report πŸ—¨οΈ Presentation πŸŽ‰ Announcement πŸ›†Motion Prediction Channel Website πŸ›†

Owner
ZhengChang
ZhengChang
Stochastic Scene-Aware Motion Prediction

Stochastic Scene-Aware Motion Prediction [Project Page] [Paper] Description This repository contains the training code for MotionNet and GoalNet of SA

Mohamed Hassan 31 Dec 9, 2022
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video Qualtitative result Paper teaser video Introduction This r

Hongsuk Choi 215 Jan 6, 2023
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond Ported from https://github.com/xinntao/BasicSR Dependencie

Holy Wu 8 Jun 7, 2022
This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Hierarchical Motion Understanding via Motion Programs (CVPR 2021) This repository contains the official implementation of: Hierarchical Motion Underst

Sumith Kulal 40 Dec 5, 2022
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance This is the codebase for video-based human motion reconstruction in human-mot

Jiachen Xu 5 Jul 14, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Kingdrone 174 Dec 22, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
[NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Code for NeurIPS 2021 Paper "Exploring Architectural Ingredients of A

Hanxun Huang 26 Dec 1, 2022
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

null 47 Dec 28, 2022