--- pipeline_tag: keypoint-detection ---
Please read [trackers/README.md](trackers/) for details. ### CrowdPose
Please read [docs/CrowdPose.md](docs/CrowdPose.md) for details. ## Installation Please check out [docs/INSTALL.md](docs/INSTALL.md) ## Model Zoo Please check out [docs/MODEL_ZOO.md](docs/MODEL_ZOO.md) ## Quick Start - **Colab**: We provide a [colab example](https://colab.research.google.com/drive/1_3Wxi4H3QGVC28snL3rHIoeMAwI2otMR?usp=sharing) for your quick start. - **Inference**: Inference demo ``` bash ./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional ``` Inference SMPL (Download the SMPL model `basicModel_neutral_lbs_10_207_0_v1.0.0.pkl` from [here](https://smpl.is.tue.mpg.de/) and put it in `model_files/`). ``` bash ./scripts/inference_3d.sh ./configs/smpl/256x192_adam_lr1e-3-res34_smpl_24_3d_base_2x_mix.yaml ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional ``` For high level API, please refer to `./scripts/demo_api.py`. To enable tracking, please refer to [this page](./trackers). - **Training**: Train from scratch ``` bash ./scripts/train.sh ${CONFIG} ${EXP_ID} ``` - **Validation**: Validate your model on MSCOCO val2017 ``` bash ./scripts/validate.sh ${CONFIG} ${CHECKPOINT} ``` Examples: Demo using `FastPose` model. ``` bash ./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/fast_res50_256x192.pth ${VIDEO_NAME} #or python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/ #or if you want to use yolox-x as the detector python scripts/demo_inference.py --detector yolox-x --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/ ``` Train `FastPose` on mscoco dataset. ``` bash ./scripts/train.sh ./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml exp_fastpose ``` More detailed inference options and examples, please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) ## Common issue & FAQ Check out [faq.md](docs/faq.md) for faq. 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](https://fang-haoshu.github.io/), Shuqin Xie, [Yu-Wing Tai](https://scholar.google.com/citations?user=nFhLmFkAAAAJ&hl=en) and [Cewu Lu](http://www.mvig.org/), [Cewu Lu](http://mvig.sjtu.edu.cn/) is the corresponding author. Currently, it is maintained by [Jiefeng Li\*](http://jeff-leaf.site/), [Hao-shu Fang\*](https://fang-haoshu.github.io/), [Haoyi Zhu](https://github.com/HaoyiZhu), [Yuliang Xiu](http://xiuyuliang.cn/about/) and [Chao Xu](http://www.isdas.cn/). The main contributors are listed in [doc/contributors.md](docs/contributors.md). ## TODO - [x] Multi-GPU/CPU inference - [x] 3D pose - [x] add tracking flag - [ ] PyTorch C++ version - [x] Add model trained on mixture dataset (Check the model zoo) - [ ] dense support - [x] small box easy filter - [x] Crowdpose support - [ ] Speed up PoseFlow - [x] Add stronger/light detectors (yolox is now supported) - [x] High level API (check the scripts/demo_api.py) We would really appreciate if you can offer any help and be the [contributor](docs/contributors.md) of AlphaPose. ## Citation Please cite these papers in your publications if it helps your research: @article{alphapose, author = {Fang, Hao-Shu and Li, Jiefeng and Tang, Hongyang and Xu, Chao and Zhu, Haoyi and Xiu, Yuliang and Li, Yong-Lu and Lu, Cewu}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, title = {AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time}, year = {2022} } @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{li2019crowdpose, title={Crowdpose: Efficient crowded scenes pose estimation and a new benchmark}, author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu}, booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, pages={10863--10872}, year={2019} } If you used the 3D mesh reconstruction module, please also cite: @inproceedings{li2021hybrik, title={Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation}, author={Li, Jiefeng and Xu, Chao and Chen, Zhicun and Bian, Siyuan and Yang, Lixin and Lu, Cewu}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3383--3393}, year={2021} } If you used the PoseFlow tracking module, please also cite: @inproceedings{xiu2018poseflow, author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu}, title = {{Pose Flow}: Efficient Online Pose Tracking}, booktitle={BMVC}, year = {2018} } ## License 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.