Data Preparation, Processing, and Visualization for MoVi Data

Overview

MoVi-Toolbox

Data Preparation, Processing, and Visualization for MoVi Data, https://www.biomotionlab.ca/movi/

MoVi is a large multipurpose dataset of human motion and video.

Here we provide tools and tutorials to use MoVi in your research projects. More specifically:

Table of Contents

Installation

Requirements

  • Python 3.*
  • MATLAB v>2017

In case you are interested in using body shape data (or also AMASS/MoVi original data) follow the instructions on AMASS Github page.

Tutorials

  • We have provided very brief tutorials on how to use the dataset in MoCap. Some of the functions are only provided in MATLAB or Python so please take a look at both tutorial files tutorial_MATLAB.m and tutorial_python.ipynb.

  • The tutorial on how to have access to the dataset is given here.

Important Notes

  • The video data for each round are provided as a single sequence (and not individual motions). In case you are interested in having synchronized video and AMASS (joint and body) data, you should trim F_PGx_Subject_x_L.avi files into single motion video files using single_videos.m function.
  • The timestamps (which separate motions) are provided by the name of “flags” in V3D files (only for f and s rounds). Please notice that “flags30” can be used for video data and “flags120” can be used for mocap data. The reason for having two types of flags is that video data were recorded in 30 fps and mocap data were recorded in 120 fps.
  • The body mesh is not provided in AMASS files by default. Please use amass_fk function to augment AMASS data with the corresponding body mesh (vertices). (the details are explained in the tutorial_python.ipynb)

Citation

Please cite the following paper if you use this code directly or indirectly in your research/projects:

@misc{ghorbani2020movi,
    title={MoVi: A Large Multipurpose Motion and Video Dataset},
    author={Saeed Ghorbani and Kimia Mahdaviani and Anne Thaler and Konrad Kording and Douglas James Cook and Gunnar Blohm and Nikolaus F. Troje},
    year={2020},
    eprint={2003.01888},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

Software Copyright License for non-commercial scientific research purposes. Before you download and/or use the Motion and Video (MoVi) dataset, please carefully read the terms and conditions stated on our website and in any accompanying documentation. If you are using the part of the dataset that was post-processed as part of AMASS, you must follow all their terms and conditions as well. By downloading and/or using the data or the code (including downloading, cloning, installing, and any other use of this GitHub repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the MoVi dataset and any associated code and software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.

Contact

The code in this repository is developed by Saeed Ghorbani.

If you have any questions you can contact us at [email protected].

Owner
Saeed Ghorbani
Graduate student in EECS department at York University
Saeed Ghorbani
Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

Patrick Kidger 89 Dec 13, 2022
PyTorch code of paper "LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering"

LiVLR-VideoQA We propose a Lightweight Visual-Linguistic Reasoning framework (LiVLR) for VideoQA. The overview of LiVLR: Evaluation on MSRVTT-QA Datas

JJ Jiang 7 Dec 30, 2022
Learnable Motion Coherence for Correspondence Pruning

Learnable Motion Coherence for Correspondence Pruning Yuan Liu, Lingjie Liu, Cheng Lin, Zhen Dong, Wenping Wang Project Page Any questions or discussi

liuyuan 41 Nov 30, 2022
This repository is for DSA and CP scripts for reference.

dsa-script-collections This Repo is the collection of DSA and CP scripts for reference. Contents Python Bubble Sort Insertion Sort Merge Sort Quick So

Aditya Kumar Pandey 9 Nov 22, 2022
Official Pytorch implementation of C3-GAN

Official pytorch implemenation of C3-GAN Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [Paper] Authors: Yunji Kim, Jun

NAVER AI 114 Dec 02, 2022
Predictive Maintenance LSTM

Predictive-Maintenance-LSTM - Predictive maintenance study for Complex case study, we've obtained failure causes by operational error and more deeply by design mistakes.

Amir M. Sadafi 1 Dec 31, 2021
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022
Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

KSTER Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper]. Usage Download the processed datas

jiangqn 23 Nov 24, 2022
Code for Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (AAAI 2019)

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (AAAI 2019) We propose Disentangled Audio-Visual System (DAVS) to ad

Hang_Zhou 750 Dec 23, 2022
TensorRT examples (Jetson, Python/C++)(object detection)

TensorRT examples (Jetson, Python/C++)(object detection)

Nobuo Tsukamoto 53 Dec 22, 2022
Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling

TGraM Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling, Qibin He, Xian Sun, Zhiyuan Yan, Beibei Li, Kun Fu Abstract Rece

Qibin He 6 Nov 25, 2022
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
LIAO Shuiying 6 Dec 01, 2022
Pytorch implementation for M^3L

Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification (CVPR 2021) Introduction This is the Py

Yuyang Zhao 45 Dec 26, 2022
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng Internati

Princeton Vision & Learning Lab 115 Jan 04, 2023
Inkscape extensions for figure resizing and editing

Academic-Inkscape: Extensions for figure resizing and editing This repository contains several Inkscape extensions designed for editing plots. Scale P

192 Dec 26, 2022
Image Recognition using Pytorch

PyTorch Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in

Sarat Chinni 1 Nov 02, 2021
TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

Fredrik Gustafsson 248 Dec 16, 2022
Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

Restormer: Efficient Transformer for High-Resolution Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan,

Syed Waqas Zamir 906 Dec 30, 2022
Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network [Paper Link] [Project Page] He Zhang, Vishwanath Sindagi, Vishal M. Patel In this

He Zhang 216 Dec 18, 2022