AlphaPose

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.

News!

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!

Contents

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

Results

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

2. 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.

Installation

1. 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 ../../../

2. Install Torch and TensorFlow(verson >= 1.2). 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

Quick Start

• 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

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

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:

./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.

Feedbacks

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!

Contributors

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.

Citation

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}
}

License

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