Motion and Shape Capture from Sparse Markers

Related tags

Deep Learningmoshpp
Overview

MoSh++

This repository contains the official chumpy implementation of mocap body solver used for AMASS:

AMASS: Archive of Motion Capture as Surface Shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black
Full paper | Video | Project website | Poster

Description

This repository holds the code for MoSh++, introduced in AMASS, ICCV'19. MoSh++ is the upgraded version of MoSh, Sig.Asia'2014. Given a labeled marker-based motion capture (mocap) c3d file and the correspondences of the marker labels to the locations on the body, MoSh can return model parameters for every frame of the mocap sequence. The current MoSh++ code works with the following models:

Installation

The Current repository requires Python 3.7 and chumpy; a CPU based auto-differentiation package. This package is assumed to be used along with SOMA, the mocap auto-labeling package. Please install MoSh++ inside the conda environment of SOMA. Clone the moshpp repository, and run the following from the root directory:

sudo apt install libeigen3-dev
sudo apt install libtbb-dev

pip install -r requirements.txt

cd src/moshpp/scan2mesh/mesh_distance
make

cd ../../../..
python setup.py install

Tutorials

This repository is a complementary package to SOMA, an automatic mocap solver. Please refer to the SOMA repository for tutorials and use cases.

Citation

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

@inproceedings{AMASS:2019,
  title={AMASS: Archive of Motion Capture as Surface Shapes},
  author={Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
  year={2019},
  month = {Oct},
  url = {https://amass.is.tue.mpg.de},
  month_numeric = {10}
}

License

Software Copyright License for non-commercial scientific research purposes. Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the MoSh++ data and software, (the "Data & Software"), software, scripts, and animations. By downloading and/or using the Data & Software (including downloading, cloning, installing, and any other use of this 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 Data & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.

The software is compiled using CGAL sources following the license in CGAL_LICENSE.pdf

Contact

The code in this repository is developed by Nima Ghorbani while at Max-Planck Institute for Intelligent Systems, Tübingen, Germany.

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

For commercial licensing, contact [email protected]

Owner
Nima Ghorbani
Research Engineer at Max-Planck Institute for Intelligent Systems. In love with math and its applications in perceiving systems.
Nima Ghorbani
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
A PyTorch based deep learning library for drug pair scoring.

Documentation | External Resources | Datasets | Examples ChemicalX is a deep learning library for drug-drug interaction, polypharmacy side effect and

AstraZeneca 597 Dec 30, 2022
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

NSGDC Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia.

Zhihao Fan 2 Nov 07, 2022
Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

DominoSearch This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense n

11 Sep 10, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

machen 11 Nov 27, 2022
A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

torchsynth The fastest synth in the universe. Introduction torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-option

torchsynth 229 Jan 02, 2023
Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab

VQGAN-CLIP-Video cat.mp4 policeman.mp4 schoolboy.mp4 forsenBOG.mp4

23 Oct 26, 2022
This repository contains the code for TACL2021 paper: SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

SummaC: Summary Consistency Detection This repository contains the code for TACL2021 paper: SummaC: Re-Visiting NLI-based Models for Inconsistency Det

Philippe Laban 24 Jan 03, 2023
Chainer implementation of recent GAN variants

Chainer-GAN-lib This repository collects chainer implementation of state-of-the-art GAN algorithms. These codes are evaluated with the inception score

399 Oct 23, 2022
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021]

piglet PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [ACL 2021] This repo contains code and data for PIGLeT. If you like

Rowan Zellers 51 Oct 08, 2022
Решения, подсказки, тесты и утилиты для тренировки по алгоритмам от Яндекса.

Решения и подсказки к тренировке по алгоритмам от Яндекса Что есть внутри Решения с подсказками и комментариями; рекомендую сначала смотреть md файл п

Yankovsky Andrey 50 Dec 26, 2022
Implementation of "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing".

DeepOrder Implementation of DeepOrder for the paper "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing". Project

6 Nov 07, 2022
InsightFace: 2D and 3D Face Analysis Project on MXNet and PyTorch

InsightFace: 2D and 3D Face Analysis Project on MXNet and PyTorch

Deep Insight 13.2k Jan 06, 2023
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
Codes and Data Processing Files for our paper.

Code Scripts and Processing Files for EEG Sleep Staging Paper 1. Folder Tree ./src_preprocess (data preprocessing files for SHHS and Sleep EDF) sleepE

Chaoqi Yang 18 Dec 12, 2022
This code is part of the reproducibility package for the SANER 2022 paper "Generating Clarifying Questions for Query Refinement in Source Code Search".

Clarifying Questions for Query Refinement in Source Code Search This code is part of the reproducibility package for the SANER 2022 paper "Generating

Zachary Eberhart 0 Dec 04, 2021
Reproduces the results of the paper "Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations".

Finite basis physics-informed neural networks (FBPINNs) This repository reproduces the results of the paper Finite Basis Physics-Informed Neural Netwo

Ben Moseley 65 Dec 28, 2022
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
g9.py - Torch interactive graphics

g9.py - Torch interactive graphics A Torch toy in the browser. Demo at https://srush.github.io/g9py/ This is a shameless copy of g9.js, written in Pyt

Sasha Rush 13 Nov 16, 2022