RMPE: Regional Multi-person Pose Estimation By Hao-Shu Fang , Shuqin Xie, Yu-Wing Tai, Cewu Lu . New version AlphaPose is released. The accuracy is 10 mAP higher than this repo. Please move to https://github.com/MVIG-SJTU/AlphaPose 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 . Results Video results available here Results on MPII dataset: Method MPII full test mAP s/frame Iqbal & Gall, ECCVw'16 43.1 10 DeeperCut, ECCV16 59.5 485 RMPE 76.7 1.5 Results on COCO test-dev 2015: Method AP @0.5:0.95 AP @0.5 AP @0.75 RMPE 61.8 83.7 69.8 Contents Installation Preparation Demo Train/Eval Citation Acknowledgements Installation 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 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 Preparation For demo only 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. For train/eval This part of our model is implemented in Torch7. Please refer to this repo for more details. Demo 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. 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 Run the python program for more results python examples/rmpe/demo.py Train/Eval 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. 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 () Citation Please cite the paper in your publications if it helps your research: @inproceedings{fang2017rmpe, title={{RMPE}: Regional Multi-person Pose Estimation}, author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu}, booktitle={ICCV}, year={2017} } Acknowledgements Thanks to Wei Liu , Alejandro Newell , Pfister, T. , Kaichun Mo , Maxime Oquab for contributing their codes. Thanks to the authors of Caffe and Torch7!