Alpha Pose is an accurate multi-person pose estimator, which is the first 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 beta PyTorch version of AlphaPose is released! Stable version will be ready in two days. Currently AlphaPose runs at about 5 fps. Realtime version is coming very soon. Stay tuned!
- AlphaPose
- Results
- Installation
- Quick Start
- Output
- Speeding Up Alpha Pose
- Feedbacks
- Contributors
- Citation
- License
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 |
Results on PoseTrack Challenge validation set:
- 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+PoseFlow | 66.7 | 73.3 | 68.3 | 61.1 | 67.5 | 67.0 | 61.3 | 66.5 |
- Task3: Pose Tracking (MOTA)
| Method | Head MOTA | Shoulder MOTA | Elbow MOTA | Wrist MOTA | Hip MOTA | Knee MOTA | Ankle MOTA | Total MOTA | Total MOTP |
|---|---|---|---|---|---|---|---|---|---|
| Detect-and-Track(FAIR) | 61.7 | 65.5 | 57.3 | 45.7 | 54.3 | 53.1 | 45.7 | 55.2 | 61.5 |
| AlphaPose+PoseFlow | 59.8 | 67.0 | 59.8 | 51.6 | 60.0 | 58.4 | 50.5 | 58.3 | 67.8 |
Note: Please read PoseFlow/README.md for details.
- Get the code and build related modules.
git clone https://github.com/MVIG-SJTU/AlphaPose.git
cd AlphaPose/human-detection/lib/
make clean
make
cd newnms/
make
cd ../../../- Install Torch and TensorFlow(verson >= 1.2). After that, install related dependencies by:
chmod +x install.sh
./install.sh- 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}
}
@ARTICLE{2018arXiv180200977X,
author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
title = {{Pose Flow}: Efficient Online Pose Tracking},
journal = {ArXiv e-prints},
eprint = {1802.00977},
year = {2018}
}
AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Cewu Lu