Poseur
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[ECCV 2022] The official repo for the paper "Poseur: Direct Human Pose Regression with Transformers".
Poseur: Direct Human Pose Regression with Transformers
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)
Introduction
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.
Installation & Quick Start
- Install following packages
pip install easydict einops
- 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_res50_coco_256x192.py 8 \
--work-dir work_dirs/poseur_res50_coco_256x192
For evaluating 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_res50_coco_256x192.py \
poseur_256x192_r50_6dec_coco.pth 4 \
--eval mAP \
--cfg-options model.filp_fuse_type=\'type2\'
For visualizing on COCO, run the following command lines:
python demo/top_down_img_demo.py \
configs/body/2d_kpt_sview_rgb_img/poseur/coco/poseur_res50_coco_256x192.py \
poseur_256x192_r50_6dec_coco.pth \
--img-root tests/data/coco/ --json-file tests/data/coco/test_coco.json \
--out-img-root vis_results_poseur
Models
COCO Keypoint Detection Results
| Name | AP | AP.5 | AP.75 | download |
|---|---|---|---|---|
| poseur_mobilenetv2_coco_256x192 | 71.9 | 88.9 | 78.6 | model |
| poseur_mobilenetv2_coco_256x192_12dec | 72.3 | 88.9 | 78.9 | model |
| poseur_res50_coco_256x192 | 75.5 | 90.7 | 82.6 | model |
| poseur_hrnet_w32_coco_256x192 | 76.8 | 91.0 | 83.5 | model |
| poseur_hrnet_w48_coco_384x288 | 78.7 | 91.6 | 85.1 | model |
| poseur_hrformer_tiny_coco_256x192_3dec | 74.2 | 90.1 | 81.4 | model |
| poseur_hrformer_small_coco_256x192_3dec | 76.6 | 91.0 | 83.4 | model |
| poseur_hrformer_big_coco_256x192 | 78.9 | 91.9 | 85.6 | model |
| poseur_hrformer_big_coco_384x288 | 79.6 | 92.1 | 85.9 | model |
Disclaimer:
- Due to the update of MMPose, the results are slightly different from our original paper.
- We use the official HRFormer implement from here, the implementation in mmpose has not been verified by us.
Citations
Please consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows.
@inproceedings{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 = {Proceedings of the European Conference on Computer Vision {(ECCV)}},
month = {October},
year={2022}
}
License
For commercial use, please contact Chunhua Shen.
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