Real-Time Multi-Person Pose Estimation System
Switch branches/tags
Nothing to show
Clone or download
Fang-Haoshu Merge pull request #172 from cclauss/modernize-python2-code
Modernize Python 2 code to get ready for Python 3
Latest commit c2a1f51 Oct 25, 2018
Type Name Latest commit message Commit time
Failed to load latest commit information.
PoseFlow update Readme Oct 2, 2018
doc Update Feb 25, 2018
examples update Feb 4, 2018
human-detection Modernize Python 2 code to get ready for Python 3 Oct 19, 2018
predict Modernize Python 2 code to get ready for Python 3 Oct 19, 2018
train Modernize Python 2 code to get ready for Python 3 Oct 19, 2018
.gitignore update PoseFlow Apr 23, 2018
LICENSE format Feb 2, 2018 update readme Oct 12, 2018 format Feb 1, 2018 Update May 3, 2018 update Feb 22, 2018


Alpha Pose is an accurate multi-person pose estimator, which is the first real-time open-source system that achieves 70+ mAP (72.3 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset.** To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.


Now PyTorch version of AlphaPose is released! It runs at 20 fps on COCO validation set (4.6 people per image on average) and achieves 71 mAP!


  1. AlphaPose
  2. Results
  3. Installation
  4. Quick Start
  5. Output
  6. Speeding Up Alpha Pose
  7. Feedbacks
  8. Contributors
  9. Citation
  10. License


Pose Estimation

Results on COCO test-dev 2015:

Method AP @0.5:0.95 AP @0.5 AP @0.75 AP medium AP large
OpenPose (CMU-Pose) 61.8 84.9 67.5 57.1 68.2
Detectron (Mask R-CNN) 67.0 88.0 73.1 62.2 75.6
AlphaPose 72.3 89.2 79.1 69.0 78.6

Results on MPII full test set:

Method Head Shoulder Elbow Wrist Hip Knee Ankle Ave
OpenPose (CMU-Pose) 91.2 87.6 77.7 66.8 75.4 68.9 61.7 75.6
Newell & Deng 92.1 89.3 78.9 69.8 76.2 71.6 64.7 77.5
AlphaPose 91.3 90.5 84.0 76.4 80.3 79.9 72.4 82.1

Pose Tracking

Results on PoseTrack Challenge validation set:

  1. Task2: Multi-Person Pose Estimation (mAP)
Method Head mAP Shoulder mAP Elbow mAP Wrist mAP Hip mAP Knee mAP Ankle mAP Total mAP
Detect-and-Track(FAIR) 67.5 70.2 62 51.7 60.7 58.7 49.8 60.6
AlphaPose 66.7 73.3 68.3 61.1 67.5 67.0 61.3 66.5
  1. Task3: Pose Tracking (MOTA)
Method Head MOTA Shoulder MOTA Elbow MOTA Wrist MOTA Hip MOTA Knee MOTA Ankle MOTA Total MOTA Total MOTP Speed(FPS)
Detect-and-Track(FAIR) 61.7 65.5 57.3 45.7 54.3 53.1 45.7 55.2 61.5 Unknown
PoseFlow(DeepMatch) 59.8 67.0 59.8 51.6 60.0 58.4 50.5 58.3 67.8 8
PoseFlow(OrbMatch) 59.0 66.8 60.0 51.8 59.4 58.4 50.3 58.0 62.2 24

Note: Please read PoseFlow/ for details.


  1. Get the code and build related modules.
git clone
cd AlphaPose/human-detection/lib/
make clean
cd newnms/
cd ../../../
  1. Install Torch and TensorFlow(verson >= 1.2). After that, install related dependencies by:
chmod +x
  1. Run to download our pre-trained models. Or download the models manually: drive|Baidu pan), final_model.t7(Google drive|Baidu pan)
chmod +x

Quick Start

  • Demo: Run AlphaPose for all images in a folder and visualize the results with:
./ --indir examples/demo/ --outdir examples/results/ --vis

The visualized results will be stored in examples/results/RENDER. To easily process images/video and display/save the results, please see doc/ If you get any problems, you can check the doc/

  • Video: You can see our video demo here.


Output (format, keypoint index ordering, etc.) in doc/

Speeding Up AlphaPose

We provide a fast mode for human-detection that disables multi-scale testing. You can turn it on by adding --mode fast.

And if you have multiple gpus on your machine or have large gpu memories, you can speed up the pose estimation step by using multi-gpu testing or large batch tesing with:

./ --indir examples/demo/ --outdir examples/results/ --gpu 0,1,2,3 --batch 5

It assumes that you have 4 gpu cards on your machine and each card can run a batch of 5 images. Here is the recommended batch size for gpu with different size of memory:

GPU memory: 4GB -- batch size: 3
GPU memory: 8GB -- batch size: 6
GPU memory: 12GB -- batch size: 9

See doc/ for more details.


If you get any problems, you can check the doc/ first. If it can not solve your problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!


AlphaPose is based on RMPE(ICCV'17), authored by Hao-shu Fang, Shuqin Xie, Yu-Wing Tai and Cewu Lu, Cewu Lu is the corresponding author. Currently, it is developed and maintained by Hao-shu Fang, Jiefeng Li, Yuliang Xiu and Ruiheng Chang.

The main contributors are listed in doc/


Please cite these papers in your publications if it helps your research:

  title={{RMPE}: Regional Multi-person Pose Estimation},
  author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},

  title = {{Pose Flow}: Efficient Online Pose Tracking},
  author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
  year = {2018}


AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Cewu Lu