By Haoshu Fang, Shuqin Xie, Yuwing Tai, Cewu Lu.

RMPE is a two steps framework for the task of multi-person pose estimation. You can use the code to train/evaluate a model for pose estimation task. For more details, please refer to our arxiv paper.

Video results available here

Results on MPII dataset:

1. Installation
2. Preparation
3. Demo
4. Train/Eval
5. Citation
6. Acknowledgements

1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT

git clone https://github.com/MVIG-SJTU/RMPE.git
cd RMPE

you can also download code from

git clone https://github.com/Fang-Haoshu/RMPE.git

2. Build the code. Please follow Caffe instruction to install all necessary packages and build it.

# Modify Makefile.config according to your Caffe installation.
# Note that the SSTN module currently ONLY have GPU implementation so you need to make&run it with GPU
cp Makefile.config.example Makefile.config
make -j8
# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make py
make test -j8
make runtest -j8
# If you have multiple GPUs installed in your machine, make runtest might fail. If so, try following:
export CUDA_VISIBLE_DEVICES=0; make runtest -j8
# If you have error: "Check failed: error == cudaSuccess (10 vs. 0)  invalid device ordinal",
# first make sure you have the specified GPUs, or try following if you have multiple GPUs:
unset CUDA_VISIBLE_DEVICES

1. Download pre-trained human detector(Google drive|Baidu cloud) and SPPE+SSTN caffe model(Google drive|Baidu cloud). By default, we assume the models are stored in $CAFFE_ROOT/models/VGG_SSD/ and $CAFFE_ROOT/models/SPPE/ accordingly.

This part of our model is implemented in Torch7. Please refer to this repo for more details.

Our experiments use both Caffe and Torch7. But we implement the whole framework in Caffe so you can run the demo easily. Note: The current caffe model of SPPE use the 2-stacked hourglass network which has a lower precision. We will be grateful if anyone can help to transfer new torch model to caffe.

1. Run the ipython notebook. It will show you how our whole framework works

cd $CAFFE_ROOT
# it shows how our framework works
jupyter notebook examples/rmpe/Regional\ Multi-person\ Pose\ Estimation.ipynb

2. Run the python program for more results

python examples/rmpe/demo.py

1. Train SPPE+SSTN. This part of our model is implemented in Torch7. Please refer to this repo for more details. We will call the directory that you cloned the repo into $SPPE_ROOT. I have written an implementation in Caffe. You can email me for the script.

2. Evaluate the model. You can modify line 45 in demo.py to evaluate our framework on whole test set. But the results will be different. To reproduce our results reported in our paper:

# First get the result of human detector
cd $CAFFE_ROOT
jupyter notebook examples/rmpe/human_detection.ipynb
# Then move the results to $SPPE_ROOT/predict/annot/
mv examples/rmpe/mpii-test0.09 $SPPE_ROOT/predict/annot/
# Next, do single person human estimation
cd $SPPE_ROOT/predict
th main.lua predict-test
#Finally, do pose NMS
python batch_nms.py
#our result is stored in txt format, to evaluate, Download MPII toolkit and put it in current directory
matlab
#In matlab
setpred()

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

@inproceedings{fang2016rmpe,
  title={{RMPE}: Regional Multi-person Pose Estimation},
  author={Haoshu Fang, Shuqin Xie, Yuwing Tai and Cewu Lu},
  journal={arXiv preprint arXiv:1612.00137},
  year={2016}
}

Thanks to Wei Liu, Alejandro Newell, Pfister, T., Kaichun Mo, Maxime Oquab for contributing their codes. Thanks to the authors of Caffe and Torch7!