Human Pose Estimation with TensorFlow
Here you can find the implementation of the Human Body Pose Estimation algorithm, presented in the DeeperCut and ArtTrack papers:
Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka and Bernt Schiele DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model. In European Conference on Computer Vision (ECCV), 2016
Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres and Bernt Schiele ArtTrack: Articulated Multi-person Tracking in the Wild. In Conference on Computer Vision and Pattern Recognition (CVPR), 2017
For more information visit http://pose.mpi-inf.mpg.de
Prerequisites
The implementation is in Python 3 and TensorFlow. We recommended using conda
to install the dependencies.
First, create a Python 3.6 environment:
conda create -n py36 python=3.6
conda activate py36
Then, install basic dependencies with conda:
conda install numpy scikit-image pillow scipy pyyaml matplotlib cython
Install TensorFlow and remaining packages with pip:
pip install tensorflow-gpu easydict munkres
When running training or prediction scripts, please make sure to set the environment variable
TF_CUDNN_USE_AUTOTUNE
to 0 (see this ticket
for explanation).
If your machine has multiple GPUs, you can select which GPU you want to run on
by setting the environment variable, eg. CUDA_VISIBLE_DEVICES=0
.
Demo code
Single-Person (if there is only one person in the image)
# Download pre-trained model files
$ cd models/mpii
$ ./download_models.sh
$ cd -
# Run demo of single person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/singleperson.py
Multiple People
# Compile dependencies
$ ./compile.sh
# Download pre-trained model files
$ cd models/coco
$ ./download_models.sh
$ cd -
# Run demo of multi person pose estimation
$ TF_CUDNN_USE_AUTOTUNE=0 python3 demo/demo_multiperson.py
Training models
Please follow these instructions
Citation
Please cite ArtTrack and DeeperCut in your publications if it helps your research:
@inproceedings{insafutdinov2017cvpr,
title = {ArtTrack: Articulated Multi-person Tracking in the Wild},
booktitle = {CVPR'17},
url = {http://arxiv.org/abs/1612.01465},
author = {Eldar Insafutdinov and Mykhaylo Andriluka and Leonid Pishchulin and Siyu Tang and Evgeny Levinkov and Bjoern Andres and Bernt Schiele}
}
@article{insafutdinov2016eccv,
title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
booktitle = {ECCV'16},
url = {http://arxiv.org/abs/1605.03170},
author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schiele}
}