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

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

If the newest vctoolkit reports errors, please use vctoolkit==0.1.5.39.

Installing pytorch with CUDA is recommended. The system can only run at ~40 fps on a CPU (i7-8700) and ~90 fps on a GPU (GTX 1080Ti).

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.

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

1. Download DIP-IMU dataset from here. We use the raw (unnormalized) data.
2. Download TotalCapture dataset from here. You need to download the real world position and orientation under Vicon Groundtruth in the website and unzip them. 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.

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.

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.

We provide live_demo.py which uses NOTIOM Legacy IMU sensors. This file contains sensor calibration details which may be useful for you.

python live_demo.py

The estimated poses and translations are sent to Unity3D for visualization using a socket in real-time. You may need to write a client to receive these data to run the live demo codes (or modify the codes a bit).

Prepare the raw AMASS dataset and modify config.py accordingly. Then, uncomment the process_amass() in preprocess.py and run:

python preprocess.py

The saved files are:

• joint.pt, which contains a list of tensors in shape [#frames, 24, 3] for 24 absolute joint 3D positions.
• pose.pt, which contains a list of tensors in shape [#frames, 24, 3] for 24 relative joint rotations (in axis-angles).
• shape.pt, which contains a list of tensors in shape [10] for the subject shape (SMPL parameter).
• tran.pt, which contains a list of tensors in shape [#frames, 3] for the global (root) 3D positions.
• vacc.pt, which contains a list of tensors in shape [#frames, 6, 3] for 6 synthetic IMU acceleration measurements (global).
• joint.pt, which contains a list of tensors in shape [#frames, 6, 3, 3] for 6 synthetic IMU orientation measurements (global).

All sequences are in 60 fps.

Please note that these synthesized data should not be directly used in training. They need normalization/coordinate frame transformation according to the paper.

1. Download the unity package from here.
2. Load the package in Unity3D (>=2019.4.16) and open the Example scene.
3. Run example_server.py. Wait till the server starts. Then play the unity scene.

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}
}