eldar / Pose Tensorflow
Projects that are alternatives of or similar to Pose Tensorflow
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}
}