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.