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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.

• AlphaPose
• News!
• Contents
• Installation
• Quick Start
• Output
• Speeding Up AlphaPose
• Feedbacks
• Contributors
• Citation
• License

1. Get the code and build related modules.

git clone https://github.com/ZhengZerong/AlphaPose.git
cd AlphaPose/human-detection/lib/
make clean
make
cd newnms/
make
cd ../../../

2. Install Torch and TensorFlow(verson >= 1.2). You may also need to install cudnn.torch to resolve compatibility issues; see this link for more details. After that, install related dependencies by:

chmod +x install.sh
./install.sh

3. Run fetch_models.sh to download our pre-trained models. Or download the models manually: output.zip(Google drive|Baidu pan), final_model.t7(Google drive|Baidu pan)

chmod +x fetch_models.sh
./fetch_models.sh

• Demo: Run AlphaPose for all images in a folder and visualize the results with:

./run.sh --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/run.md. If you get any problems, you can check the doc/faq.md.

• Video: You can see our video demo here.

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

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:

./run.sh --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/run.md for more details.

If you get any problems, you can check the doc/faq.md 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/contributors.md.

Please cite these papers 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}
}

@inproceedings{xiu2018poseflow,
  title = {{Pose Flow}: Efficient Online Pose Tracking},
  author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
  booktitle={BMVC},
  year = {2018}
}

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