Realtime Multi-Person Pose Estimation

By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh.

Introduction

Code repo for winning 2016 MSCOCO Keypoints Challenge, 2016 ECCV Best Demo Award, and 2017 CVPR Oral paper.

Watch our video result in YouTube or our website.

We present a bottom-up approach for realtime multi-person pose estimation, without using any person detector. For more details, refer to our CVPR'17 paper, our oral presentation video recording at CVPR 2017 or our presentation slides at ILSVRC and COCO workshop 2016.

This project is licensed under the terms of the license.

Other Implementations

Thank you all for the efforts! If you have new implementation and want to share with others, feel free to make a pull request or email me!

Our new C++ library OpenPose (testing only)
Tensorflow [version 1] | [version 2] | [version 3]
Pytorch [version 1] | [version 2]
Chainer [version 1]
MXnet [version 1]
MatConvnet [version 1]

Testing
Training
Citation

Testing

C++ (realtime version, for demo purpose)

In May 2017, we released an updated library: OpenPose
Old modified caffe version (not maintained anymore, please use OpenPose instead): caffe_rtpose. Follow the instruction on that repo.
Three input options: images, video, webcam

Matlab (slower, for COCO evaluation)

Compatible with general Caffe. Compile matcaffe.
Run cd testing; get_model.sh to retrieve our latest MSCOCO model from our web server.
Change the caffepath in the config.m and run demo.m for an example usage.

Python

cd testing/python
ipython notebook
Open demo.ipynb and execute the code

Training

Network Architecture

Training Steps

Run cd training; bash getData.sh to obtain the COCO images in dataset/COCO/images/, keypoints annotations in dataset/COCO/annotations/ and COCO official toolbox in dataset/COCO/coco/.
Run getANNO.m in matlab to convert the annotation format from json to mat in dataset/COCO/mat/.
Run genCOCOMask.m in matlab to obatin the mask images for unlabeled person. You can use 'parfor' in matlab to speed up the code.
Run genJSON('COCO') to generate a json file in dataset/COCO/json/ folder. The json files contain raw informations needed for training.
Run python genLMDB.py to generate your LMDB. (You can also download our LMDB for the COCO dataset (189GB file) by: bash get_lmdb.sh)
Download our modified caffe: caffe_train. Compile pycaffe. It will be merged with caffe_rtpose (for testing) soon.
Run python setLayers.py --exp 1 to generate the prototxt and shell file for training.
Download VGG-19 model, we use it to initialize the first 10 layers for training.
Run bash train_pose.sh 0,1 (generated by setLayers.py) to start the training with two gpus.

Citation

Please cite the paper in your publications if it helps your research:

@inproceedings{cao2017realtime,
  author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {CVPR},
  title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  year = {2017}
  }
  
@inproceedings{wei2016cpm,
  author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
  booktitle = {CVPR},
  title = {Convolutional pose machines},
  year = {2016}
  }

zhangkaij/Realtime_Multi-Person_Pose_Estimation