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This short documentation describes steps necessary to compile and run CNN-based body part detectors presented in the DeeperCut paper:
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
For more information visit https://pose.mpi-inf.mpg.de
Installation Instructions
This code was developed under Linux (Debian wheezy, 64 bit) and was tested only in this environment.
Build Caffe and Python bindings as described in the official documentation. You will have to disable CuDNN support and enable C++ 11.
$ make all pycaffe
Install Python Click package (required for demo only)
$ pip install click
Set PYTHONPATH variable
$ export PYTHONPATH=`pwd`/python
Download Caffe Models
$ cd models/deepercut
$ ./download_models.sh
Run Demo
$ cd python/pose
$ python ./pose_demo.py image.png --out_name=prediction
Citing
@inproceedings{insafutdinov2016deepercut,
author = {Eldar Insafutdinov and Leonid Pishchulin and Bjoern Andres and Mykhaylo Andriluka and Bernt Schieke},
title = {DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2016},
url = {https://arxiv.org/abs/1605.03170}
}
@inproceedings{pishchulin16cvpr,
author = {Leonid Pishchulin and Eldar Insafutdinov and Siyu Tang and Bjoern Andres and Mykhaylo Andriluka and Peter Gehler and Bernt Schiele},
title = {DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016},
url = {https://arxiv.org/abs/1511.06645}
}
About
CNN architecture for articulated human pose estimation