Poseur: Direct Human Pose Regression with Transformers,
Weian Mao*, Yongtao Ge*, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton van den Hengel
In: European Conference on Computer Vision (ECCV), 2022
arXiv preprint (arXiv 2201.07412)
(* equal contribution)
This is a preview for Poseur, which currently including Poseur with R-50 backbone for both training and inference. More models with various backbones will be released soon. This project is bulit upon MMPose with commit ID eeebc652842a9724259ed345c00112641d8ee06d.
First, follow the MMPose instruction to install the project and set up the datasets (MS-COCO).
For training on COCO, run:
./tools/dist_train.sh \
configs/body/2d_kpt_sview_rgb_img/poseur/coco/poseur_r50_coco_256x192.py 8 \
--work-dir work_dirs/poseur_r50_coco_256x192
For evaluation on COCO, run the following command lines:
wget https://cloudstor.aarnet.edu.au/plus/s/UXr1Dn9w6ja4fM9/download -O poseur_256x192_r50_6dec_coco.pth
./tools/dist_test.sh configs/body/2d_kpt_sview_rgb_img/poseur/coco/poseur_r50_coco_256x192.py \
poseur_256x192_r50_6dec_coco.pth 4 \
--eval mAP \
--cfg-options model.filp_fuse_type=\'type2\'
Name | AP | AP.5 | AP.75 | download |
---|---|---|---|---|
Poseur_R50_COCO_256x192 | 75.5 | 90.7 | 82.6 | model |
Disclaimer:
- Due to the update of MMPose, the result of R50 is slightly higher than our original paper.
Please consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows.
@article{mao2022poseur,
title={Poseur: Direct human pose regression with transformers},
author={Mao, Weian and Ge, Yongtao and Shen, Chunhua and Tian, Zhi and Wang, Xinlong and Wang, Zhibin and Hengel, Anton van den},
journal={arXiv preprint arXiv:2201.07412},
year={2022}
}
For academic use, this project is licensed under the 2-clause BSD License. For commercial use, please contact Chunhua Shen.