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.
- Feb 2019: CrowdPose is integrated into AlphaPose Now!
- Dec 2018: General version of PoseFlow is released! 3X Faster and support pose tracking results visualization!
- Sep 2018: 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!
- AlphaPose
- News!
- Contents
- Results
- Installation
- Quick Start
- Output
- Speeding Up AlphaPose
- 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 | 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 | 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/README.md for details.
Results on CrowdPose Validation:
Compare with state-of-the-art methods
| Method | AP @0.5:0.95 | AP @0.5 | AP @0.75 | AR @0.5:0.95 | AR @0.5 | AR @0.75 |
|---|---|---|---|---|---|---|
| Detectron (Mask R-CNN) | 57.2 | 83.5 | 60.3 | 65.9 | 89.3 | 69.4 |
| Simple Pose (Xiao et al.) | 60.8 | 81.4 | 65.7 | 67.3 | 86.3 | 71.8 |
| Ours | 66.0 | 84.2 | 71.5 | 72.7 | 89.5 | 77.5 |
Compare with open-source systems
| Method | AP @Easy | AP @Medium | AP @Hard | FPS |
|---|---|---|---|---|
| OpenPose (CMU-Pose) | 62.7 | 48.7 | 32.3 | 5.3 |
| Detectron (Mask R-CNN) | 69.4 | 57.9 | 45.8 | 2.9 |
| Ours | 75.5 | 66.3 | 57.4 | 10.1 |
Note: Please read doc/CrowdPose.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}
}
@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, please drop an e-mail at mvig.alphapose[at]gmail.[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.