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
arXiv:1605.03170, 2016
For more information visit http://pose.mpi-inf.mpg.de
- 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
$ cd models/deepercut
$ ./download_models.sh
$ cd python/pose
$ python ./pose_demo.py image.png --out_name=prediction
@article{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},
journal = {arXiv},
year = {2016},
url = {http://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 = {http://arxiv.org/abs/1511.06645}
}