Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors
Vladimir Guzov*, Aymen Mir*, Torsten Sattler , Gerard Pons-Moll
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2021
(* joint first authors with equal contribution)
HPS jointly estimates the full 3D human pose and location of a subject within large 3D scenes, using only wearable sensors. Left: subject wearing IMUs and a head mounted camera. Right: using the camera, HPS localizes the human in a pre-built map of the scene (bottom left). The top row shows the split images of the real and estimated virtual camera
Download the scenes, predefined vertices, all the IMU .txt files and .MVNX files, the video files and the camera localization .json files
Change the corresponding global variables denoting locations of these files in global_vars.py
Create conda environment
conda env create -f hps_env.yml
Run Preprocessing code.
python preprocess/preprocess.py --file_name seq_name
Run Initialization code.
python preprocess/Initialization.py --file_name seq_name
Compute sitting frames
python preprocess/sit_frames.py --file_name seq_name
Compute scene normals
python preprocess/scene_normals.py
Create a config file. A sample file is found in configs folder
Run the optimization code as follows
python main --config configs/sample.txt
If you find our code useful, please consider citing our paper
@inproceedings{HPS,
title = {Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors },
author = {Guzov, Vladimir and Mir, Aymen and Sattler, Torsten and Pons-Moll, Gerard},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {jun},
organization = {{IEEE}},
year = {2021},
}
This code is available for non-commercial scientific research purposes as defined in the LICENSE file. By downloading and using this code you agree to the terms in the LICENSE.
The smplpytorch code comes from Gul Varol's repository
The ChamferDistancePytorch codes from Thibault Groueix's repository