This repository is a real-time demo for our paper that was published at WACV 2021 conference
The output of our module is in SMPL-X parametric body mesh model:
Combined system runs at 30 fps on a 2080ti GPU and 8 core @ 4GHz CPU.
git clone https://github.com/rmbashirov/rgbd-kinect-pose.git
cd rgbd-kinect-pose
git submodule update --force --init --remote
sudo groupadd docker
sudo usermod -aG docker $USER
./run_local.sh
from docker
dirpython -m pyk4a.viewer --vis_color --no_bt --no_depth
inside docker containersmplx_kinect_demo_data.tar.gz
mkdir /your/unpacked/dir
, tar -zxf smplx_kinect_demo_data.tar.gz -C /your/unpacked/dir
/your/unpacked/dir/minimal_hand/model
/your/unpacked/dir/pykinect/body_models/smplx
/your/unpacked/dir
should look like thisdata_dirpath
and output_dirpath
variables in config file:
data_dirpath
is a path to /your/unpacked/dir
output_dirpath
is used to check timings or to store result imagesVOLUMES
variable heresrc
dir run ./run_server.sh
, the latter will enter docker container and will use config file where shape of the person is loaded from an external file: in our work we did not focus on person's shape estimationApart from our main body pose estimation contribution you can find this repository useful for:
If you find the project helpful, please consider citing us:
@inproceedings{bashirov2021real,
title={Real-Time RGBD-Based Extended Body Pose Estimation},
author={Bashirov, Renat and Ianina, Anastasia and Iskakov, Karim and Kononenko, Yevgeniy and Strizhkova, Valeriya and Lempitsky, Victor and Vakhitov, Alexander},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={2807--2816},
year={2021}
}
Non-commercial use only