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
Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

Abandoning the Bayer-Filter to See in the Dark (CVPR 2022) Paper: https://arxiv.org/abs/2203.04042 (Arxiv version) This code includes the training and

74 Dec 15, 2022
Official PyTorch implementation of StyleGAN3

Modified StyleGAN3 Repo Changes Made tied to python 3.7 syntax .jpgs instead of .pngs for training sample seeds to recreate the 1024 training grid wit

Derrick Schultz (he/him) 83 Dec 15, 2022
Semi-Supervised Learning with Ladder Networks in Keras. Get 98% test accuracy on MNIST with just 100 labeled examples !

Semi-Supervised Learning with Ladder Networks in Keras This is an implementation of Ladder Network in Keras. Ladder network is a model for semi-superv

Divam Gupta 101 Sep 07, 2022
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"

Sun Yi 201 Nov 21, 2022
PolyTrack: Tracking with Bounding Polygons

PolyTrack: Tracking with Bounding Polygons Abstract In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segme

Gaspar Faure 13 Sep 15, 2022
An open-access benchmark and toolbox for electricity price forecasting

epftoolbox The epftoolbox is the first open-access library for driving research in electricity price forecasting. Its main goal is to make available a

97 Dec 05, 2022
Automated detection of anomalous exoplanet transits in light curve data.

Automatically detecting anomalous exoplanet transits This repository contains the source code for the paper "Automatically detecting anomalous exoplan

1 Feb 01, 2022
Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

KAIROS MineRL BASALT Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL B

Vinicius G. Goecks 37 Oct 30, 2022
Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets"

Replication Package for "An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Data

2 Oct 06, 2022
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

35 Jan 03, 2023
This is a model made out of Neural Network specifically a Convolutional Neural Network model

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternativ

9 Oct 18, 2022
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight

Revisiting RCAN: Improved Training for Image Super-Resolution Introduction Image super-resolution (SR) is a fast-moving field with novel architectures

Zudi Lin 76 Dec 01, 2022
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating

No RL No Simulation (NRNS) Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating NRNS is a heriarch

Meera Hahn 20 Nov 29, 2022
The official code for paper "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling".

R2D2 This is the official code for paper titled "R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Mode

Alipay 49 Dec 17, 2022
Laplace Redux -- Effortless Bayesian Deep Learning

Laplace Redux - Effortless Bayesian Deep Learning This repository contains the code to run the experiments for the paper Laplace Redux - Effortless Ba

Runa Eschenhagen 28 Dec 07, 2022
StorSeismic: An approach to pre-train a neural network to store seismic data features

StorSeismic: An approach to pre-train a neural network to store seismic data features This repository contains codes and resources to reproduce experi

Seismic Wave Analysis Group 11 Dec 05, 2022
1st Solution For NeurIPS 2021 Competition on ML4CO Dual Task

KIDA: Knowledge Inheritance in Data Aggregation This project releases our 1st place solution on NeurIPS2021 ML4CO Dual Task. Slide and model weights a

MEGVII Research 24 Sep 08, 2022
MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

ZhengChang 20 Nov 25, 2022