A real-time motion capture system that estimates poses and global translations using only 6 inertial measurement units

Overview

TransPose

Code for our SIGGRAPH 2021 paper "TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors". This repository contains the system implementation, evaluation, and some example IMU data which you can easily run with. Project Page

Live Demo 1Live Demo 2

Usage

Install dependencies

We use python 3.7.6. You should install the newest pytorch chumpy vctoolkit open3d.

Prepare SMPL body model

  1. Download SMPL model from here. You should click SMPL for Python and download the version 1.0.0 for Python 2.7 (10 shape PCs). Then unzip it.
  2. In config.py, set paths.smpl_file to the model path.

Prepare pre-trained network weights

  1. Download weights from here.
  2. In config.py, set paths.weights_file to the weights path.

Prepare test datasets (optional)

  1. Download DIP-IMU dataset from here. We use the raw (unnormalized) data.
  2. Download TotalCapture dataset from here. The ground-truth SMPL poses used in our evaluation are provided by the DIP authors. So you may also need to contact the DIP authors for them.
  3. In config.py, set paths.raw_dipimu_dir to the DIP-IMU dataset path; set paths.raw_totalcapture_dip_dir to the TotalCapture SMPL poses (from DIP authors) path; and set paths.raw_totalcapture_official_dir to the TotalCapture official gt path. Please refer to the comments in the codes for more details.

Run the example

To run the whole system with the provided example IMU measurement sequence, just use:

python example.py

The rendering results in Open3D may be upside down. You can use your mouse to rotate the view.

Run the evaluation

You should preprocess the datasets before evaluation:

python preprocess.py
python evaluate.py

Both offline and online results for DIP-IMU and TotalCapture test datasets will be printed.

Citation

If you find the project helpful, please consider citing us:

@article{TransPoseSIGGRAPH2021,
    author = {Yi, Xinyu and Zhou, Yuxiao and Xu, Feng},
    title = {TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors},
    journal = {ACM Transactions on Graphics}, 
    year = {2021}, 
    month = {08},
    volume = {40},
    number = {4}, 
    articleno = {86},
    publisher = {ACM}
} 
OpenMMLab Computer Vision Foundation

English | 简体中文 Introduction MMCV is a foundational library for computer vision research and supports many research projects as below: MMCV: OpenMMLab

OpenMMLab 4.6k Jan 09, 2023
Interpretation of T cell states using reference single-cell atlases

Interpretation of T cell states using reference single-cell atlases ProjecTILs is a computational method to project scRNA-seq data into reference sing

Cancer Systems Immunology Lab 139 Jan 03, 2023
SNIPS: Solving Noisy Inverse Problems Stochastically

SNIPS: Solving Noisy Inverse Problems Stochastically This repo contains the official implementation for the paper SNIPS: Solving Noisy Inverse Problem

Bahjat Kawar 35 Nov 09, 2022
To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

Kunal Wadhwa 2 Jan 05, 2022
Hl classification bc - A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

A Network-Based High-Level Data Classification Algorithm Using Betweenness Centr

Esteban Vilca 3 Dec 01, 2022
A spherical CNN for weather forecasting

DeepSphere-Weather - Deep Learning on the sphere for weather/climate applications. The code in this repository provides a scalable and flexible framew

DeepSphere 47 Dec 25, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Visually Grounded Bert Language Model This repository is the official implementation of Explainable Semantic Space by Grounding Language to Vision wit

17 Dec 17, 2022
Predictive AI layer for existing databases.

MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning

MindsDB Inc 12.2k Jan 03, 2023
Code for the IJCAI 2021 paper "Structure Guided Lane Detection"

SGNet Project for the IJCAI 2021 paper "Structure Guided Lane Detection" Abstract Recently, lane detection has made great progress with the rapid deve

Jinming Su 27 Dec 08, 2022
H&M Fashion Image similarity search with Weaviate and DocArray

H&M Fashion Image similarity search with Weaviate and DocArray This example shows how to do image similarity search using DocArray and Weaviate as Doc

Laura Ham 18 Aug 11, 2022
Context-Sensitive Misspelling Correction of Clinical Text via Conditional Independence, CHIL 2022

cim-misspelling Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022. This model (

Juyong Kim 11 Dec 19, 2022
Scalable machine learning based time series forecasting

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

Nixtla 145 Dec 24, 2022
Uses OpenCV and Python Code to detect a face on the screen

Simple-Face-Detection This code uses OpenCV and Python Code to detect a face on the screen. This serves as an example program. Important prerequisites

Denis Woolley (CreepyD) 1 Feb 12, 2022
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021) Citation Please cite as: @inproceedings{liu2020understan

Sunbow Liu 22 Nov 25, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On

Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On [Project website] [Dataset] [Video] Abstract We propose a new g

71 Dec 24, 2022
Code for the Lovász-Softmax loss (CVPR 2018)

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Maxim Berman 1.3k Jan 04, 2023
Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

RHGN Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling Dependencies torch==1.6.0 torchvision==0.7.0 dgl==0.7.1

Big Data and Multi-modal Computing Group, CRIPAC 6 Nov 29, 2022
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022