UDP-Pose

This is the pytorch implementation for UDP++, which won the Fisrt place in COCO Keypoint Challenge at ECCV 2020 Workshop.

Top-Down

Results on MPII val dataset

Results on COCO val2017 with detector having human AP of 65.1 on COCO val2017 dataset

Note:

• Flip test is used.
• Person detector has person AP of 65.1 on COCO val2017 dataset.
• GFLOPs is for convolution and linear layers only.
• UDPv1: v0:LOSS.KPD=4.0, v1:LOSS.KPD=3.5

Results on COCO test-dev with detector having human AP of 65.1 on COCO val2017 dataset

Note:

• Flip test is used.
• Person detector has person AP of 65.1 on COCO val2017 dataset.
• GFLOPs is for convolution and linear layers only.

Bottom-Up

HRNet

HigherHRNet

Note:

• ori : Result from original HigherHrnet
• mmpose : Pretrained models from mmpose
• P2I : PROJECT2IMAGE
• we use mmpose for codebase
• the configurations of the baseline are HRNet-W32-512x512-batch16-lr0.001
• Speed is tested with dist_test in mmpose codebase and 8 Gpus + 16 batchsize

Quick Start

(Recommend) For mmpose, please refer to MMPose

For hrnet, please refer to Hrnet

For RSN, please refer to RSN

Data preparation For coco, we provide the human detection result and pretrained model at BaiduDisk(dsa9)

Citation

If you use our code or models in your research, please cite with:

@inproceedings{cai2020learning,
  title={Learning Delicate Local Representations for Multi-Person Pose Estimation},
  author={Yuanhao Cai and Zhicheng Wang and Zhengxiong Luo and Binyi Yin and Angang Du and Haoqian Wang and Xinyu Zhou and Erjin Zhou and Xiangyu Zhang and Jian Sun},
  booktitle={ECCV},
  year={2020}
}
@article{huang2020joint,
  title={Joint coco and lvis workshop at eccv 2020: Coco keypoint challenge track technical report: Udp+},
  author={Huang, Junjie and Shan, Zengguang and Cai, Yuanhao and Guo, Feng and Ye, Yun and Chen, Xinze and Zhu, Zheng and Huang, Guan and Lu, Jiwen and Du, Dalong},
  year={2020}
}