Skip to content

eldar/pose-tensorflow

Folders and files

NameName
Last commit message
Last commit date

Latest commit

author
Eldar Insafutdinov
Mar 19, 2019
6a8f5ee · Mar 19, 2019

History

23 Commits
Jun 22, 2017
Mar 17, 2019
Mar 19, 2019
Jun 21, 2017
Jun 27, 2017
Mar 17, 2019
Jun 23, 2017
Nov 8, 2017
Mar 17, 2019
Jun 22, 2017
May 12, 2017
Mar 19, 2019
Jul 11, 2017
Mar 17, 2019
Mar 17, 2019
Mar 17, 2019
Mar 17, 2019
Mar 17, 2019
Mar 17, 2019
Mar 17, 2019

Repository files navigation

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}
}