https://github.com/eldar/pose-tensorflow

Human Pose estimation with TensorFlow framework
https://github.com/eldar/pose-tensorflow

deep-learning pose-estimation tensorflow

Last synced: 6 days ago
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Human Pose estimation with TensorFlow framework

• Host: GitHub
• URL: https://github.com/eldar/pose-tensorflow
• Owner: eldar
• License: lgpl-3.0
• Created: 2017-03-09T19:52:15.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2019-12-07T23:30:02.000Z (about 5 years ago)
• Last Synced: 2025-01-13T01:03:36.586Z (13 days ago)
• Topics: deep-learning, pose-estimation, tensorflow
• Language: C++
• Size: 1.51 MB
• Stars: 1,139
• Watchers: 56
• Forks: 383
• Open Issues: 65
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• AwesomeCppGameDev - pose-tensorflow

README

        
# Human Pose Estimation with TensorFlow

![](images/teaser.png)

Here you can find the implementation of the Human Body Pose Estimation algorithm,

presented in the [DeeperCut](http://arxiv.org/abs/1605.03170) and [ArtTrack](http://arxiv.org/abs/1612.01465) 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:

```bash

conda create -n py36 python=3.6

conda activate py36

```

Then, install basic dependencies with conda:

```bash

conda install numpy scikit-image pillow scipy pyyaml matplotlib cython

```

Install TensorFlow and remaining packages with pip:

```bash

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](https://github.com/tensorflow/tensorflow/issues/5048)

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](models/README.md)

## 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}

    }