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项目作者： ilovepose

项目描述 ：
Distribution-Aware Coordinate Representation for Human Pose Estimation
高级语言： Cuda
项目地址: git://github.com/ilovepose/DarkPose.git
创建时间： 2019-10-31T08:41:35Z
项目社区：https://github.com/ilovepose/DarkPose
开源协议：Apache License 2.0
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Distribution Aware Coordinate Representation for Human Pose Estimation


Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models!

News

  • [2019/10/14] DarkPose is now on ArXiv.
  • [2019/10/15] Project page is created.
  • [2019/10/27] DarkPose achieve 76.4 on the COCO test-challenge (2nd place entry of COCO Keypoints Challenge ICCV 2019)!
  • [2020/02/24] DarkPose accepted by CVPR2020.
  • [2020/06/17] Code is released.
  • [2020/08/07] Pretrained models are provided.

Introduction

    This work fills the gap by studying the coordinate representation with a particular focus on the heatmap. We formulate a novel Distribution-Aware coordinate Representation of Keypoint (DARK) method. Serving as a model-agnostic plug-in, DARK significantly improves the performance of a variety of state-of-the-art human pose estimation models!

Illustrating the architecture of the proposed DARK

Our CVPR2019 work Fast Human Pose Estimation can work seamlessly with DARK, which is available at Github

Main Results

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Baseline Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR
Hourglass(4 Blocks) 128×96 13.0M 2.7 66.2 87.6 75.1 63.8 71.4 72.8
Hourglass(4 Blocks) + DARK 128×96 13.0M 2.7 69.6 87.8 77.0 67.0 75.4 75.7
Hourglass(8 Blocks) 128×96 25.1M 4.9 67.6 88.3 77.4 65.2 73.0 74.0
Hourglass(8 Blocks) + DARK 128×96 25.1M 4.9 70.8 87.9 78.3 68.3 76.4 76.6
SimpleBaseline-R50 128×96 34.0M 2.3 59.3 85.5 67.4 57.8 63.8 66.6
SimpleBaseline-R50 + DARK 128×96 34.0M 2.3 62.6 86.1 70.4 60.4 67.9 69.5
SimpleBaseline-R101 128×96 53.0M 3.1 58.8 85.3 66.1 57.3 63.4 66.1
SimpleBaseline-R101 + DARK 128×96 53.0M 3.1 63.2 86.2 71.1 61.2 68.5 70.0
SimpleBaseline-R152 128×96 68.6M 3.9 60.7 86.0 69.6 59.0 65.4 68.0
SimpleBaseline-R152 + DARK 128×96 68.6M 3.9 63.1 86.2 71.6 61.3 68.1 70.0
HRNet-W32 128×96 28.5M 1.8 66.9 88.7 76.3 64.6 72.3 73.7
HRNet-W32 + DARK 128×96 28.5M 1.8 70.7 88.9 78.4 67.9 76.6 76.7
HRNet-W48 128×96 63.6M 3.6 68.0 88.9 77.4 65.7 73.7 74.7
HRNet-W48 + DARK 128×96 63.6M 3.6 71.9 89.1 79.6 69.2 78.0 77.9
HRNet-W32 256×192 28.5M 7.1 74.4 90.5 81.9 70.8 81.0 79.8
HRNet-W32 + DARK 256×192 28.5M 7.1 75.6 90.5 82.1 71.8 82.8 80.8
HRNet-W32 384×288 28.5M 16.0 75.8 90.6 82.5 72.0 82.7 80.9
HRNet-W32 + DARK 384×288 28.5M 16.0 76.6 90.7 82.8 72.7 83.9 81.5
HRNet-W48 384×288 63.6M 32.9 76.3 90.8 82.9 72.3 83.4 81.2
HRNet-W48 + DARK 384×288 63.6M 32.9 76.8 90.6 83.2 72.8 84.0 81.7

Note:

  • Flip test is used.
  • Person detector has person AP of 56.4 on COCO val2017 dataset.
  • GFLOPs is for convolution and linear layers only.

Results on COCO test-dev2017 with detector having human AP of 60.9 on COCO test-dev2017 dataset

Baseline Input size #Params GFLOPs AP Ap.5 AP.75 AP(M) AP(L) AR
HRNet-W48 384x288 63.6M 32.9 75.5 92.5 83.3 71.9 81.5 80.5
HRNet-W48 + DARK 384x288 63.6M 32.9 76.2 92.5 83.6 72.5 82.4 81.1
HRNet-W48* 384x288 63.6M 32.9 77.0 92.7 84.5 73.4 83.1 82.0
HRNet-W48 + DARK* 384x288 63.6M 32.9 77.4 92.6 84.6 73.6 83.7 82.3
HRNet-W48 + DARK*- 384x288 63.6M 32.9 78.2 93.5 85.5 74.4 84.2 83.5
HRNet-W48 + DARK*-+ 384x288 63.6M 32.9 78.9 93.8 86.0 75.1 84.4 83.5

Note:

  • Flip test is used.
  • Person detector has person AP of 60.9 on COCO test-dev2017 dataset.
  • GFLOPs is for convolution and linear layers only.
  • * means using additional data from AI challenger for training.
  • - means the detector ensemble with HTC and SNIPER.
  • + means using model ensemble.

Results on MPII val

PCKh Baseline Head Shoulder Elbow Wrist Hip Knee Ankle Mean
0.5 HRNet_w32 97.1 95.9 90.3 86.5 89.1 87.1 83.3 90.3
0.5 HRNet_w32 + DARK 97.2 95.9 91.2 86.7 89.7 86.7 84.0 90.6
0.1 HRNet_w32 51.1 42.7 42.0 41.6 17.9 29.9 31.0 37.7
0.1 HRNet_w32 + DARK 55.2 47.8 47.4 45.2 20.1 33.4 35.4 42.0

Note:

  • Flip test is used.
  • Input size is 256x256
  • GFLOPs is for convolution and linear layers only.

Quick start

1. Preparation

1.1 Prepare the dataset

For the MPII dataset, the original annotation files are in matlab format. We have converted them into json format, you also need to download them from OneDrive or GoogleDrive.
Extract them under {POSE_ROOT}/data, your directory tree should look like this: 

  1. ${POSE_ROOT}/data/mpii
  2. ├── images
  3. └── mpii_human_pose_v1_u12_1.mat
  4. |—— annot
  5. |   |—— gt_valid.mat
  6. └── |—— test.json
  7.     |   |—— train.json
  8.     |   |—— trainval.json
  9.     |   |—— valid.json
  10.     └── images
  11.         |—— 000001163.jpg
  12.         |—— 000003072.jpg

For the COCO dataset, your directory tree should look like this: 

  1. ${POSE_ROOT}/data/coco
  2. ├── annotations
  3. ├── images
  4. │   ├── test2017
  5. │   ├── train2017
  6. │   └── val2017
  7. └── person_detection_results
  8. `

1.2 Download the pretrained models

Pretrained models are provided.

1.3 Prepare the environment

Setting the parameters in the file prepare_env.sh as follows:

  1. # DATASET_ROOT=$HOME/datasets
  2. # COCO_ROOT=${DATASET_ROOT}/MSCOCO
  3. # MPII_ROOT=${DATASET_ROOT}/MPII
  4. # MODELS_ROOT=${DATASET_ROOT}/models

Then execute:

  1. bash prepare_env.sh

If you like, you can prepare the environment step by step

Citation

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

  1. @InProceedings{Zhang_2020_CVPR,
  2.     author = {Zhang, Feng and Zhu, Xiatian and Dai, Hanbin and Ye, Mao and Zhu, Ce},
  3.     title = {Distribution-Aware Coordinate Representation for Human Pose Estimation},
  4.     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  5.     month = {June},
  6.     year = {2020}
  7. }

Acknowledgement

Thanks for the open-source HRNet