/deepcut-cnn

CNN architecture for articulated human pose estimation

Primary LanguageC++OtherNOASSERTION

DeeperCut Part Detectors

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 http://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 = {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}
}