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PoP-Net

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Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image

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# MP-3DHP: Multi-Person 3D Human Pose Dataset

## Introduction

Multi-Person 3D HumanPose (MP-3DHP) Dataset was introduced in the WACV 2022 paper:

"Pop-net: pose over parts network for multi-person 3D pose estimation from a depth image", Y. Guo, etal., WACV, 2022 [[wacv](https://openaccess.thecvf.com/content/WACV2022/html/Guo_PoP-Net_Pose_Over_Parts_Network_for_Multi-Person_3D_Pose_Estimation_WACV_2022_paper.html)] [[arxiv](https://arxiv.org/abs/2012.06734)]

MP-3DHP Dataset is a depth sensor-based dataset, which was constructed to facilitate the development of multi-person 3D pose estimation methods targeting real-world challenges. The dataset includes 177k training data and 33k validation data where both the 3D human poses and body segments are avaliable. The dataset also include 9k clean background data and 4k testing data including multi-person 3D poses.

Specifically, MP-3DHP was constructed to ensure data sufficiency and diversity in the training data considering the variance in human poses, object scales, camera angles, truncation scenarios, background scenes, and dynamic occlusions. The availibility of human body segments the background images enables the effective multi-person and background data augmentation which is crutial for the generalization from single-person training to multi-person testing task. For the details in dataset construction, data augmentation methods, and the evaluation metrics, please refer to our paper.

<img src="figs/dataset_visualization.png" width="700" />
 
 Figure 1: MP-3DHP dataset. (Top) Five single-human examples recorded from different locations from the training set. (Bottom) Two background scenes, and three multi-person testing examples are shown.

In the training set, a human subject is recorded at four different locations relative to the camera plus a set of 
free-style movements. Besides labels of human joints, foreground human masks are also 
provided in the training set. Such setup enables background and multi-person augmentation methods. Two examples of 
background augmentation and two examples of multi-person augmentation are visualized in the following figure.

<img src="figs/bg_aug.png" width="250" />
 <img src="figs/mp_aug.png" width="250" />
 
 Figure 2: Augmented training samples. (Left) Single-person training samples augmented with a random background scene. (Right) Augmented multi-person training sample composed from multiple single-person training samples and a random background scene.

The summary of the dataset is shown in the Table below.

<img src="figs/dataset_summary.png" width="400" />
 
 Table 1: MP-3DHP Summary. The total number of images (Img), human subjects (Sbj), recording locations (Loc)}, self-orientations (Ori), action types (Act), scenes (Sn) are summarized. Additional label type (L+) indicates whether a set has segmentation (seg) or multi-person (mp) labels.

## Data download and preparation

Three data folders with the following directory structure are provided to demonstrate the usage of this dataset. You 
are welcome to use the torrent file(**"./dataset.torrent"**) to download the dataset(~800GB). 
We recommended you use [bittorent](https://www.bittorrent.com/) as the download client.

The downloaded dataset includes three folders which are expected to be placed under the root directory of the repo together with 'util' folder. The detailed directory structures are explained as below:
```
    dataset
    ├── train_val
    │   ├── depth_maps
    │   ├── seg_maps
    │   └── bg_maps
    │── test_bgaug
    │   └── depth_maps
    │── test_mpaug
    │   └── depth_maps
    └── test_mpreal
        └── depth_maps

```

The 'dataset' folder contains all the actual data files.

```
    labels
    ├── train_val
    │   ├── labels_bg.json
    │   ├── labels_train.json, labels_train_cl.json, labels_train_cr.json, labels_train_fl.json, labels_train_fr.json, labels_train_free.json
    │   └── labels_test.json, labels_test_cl.json, labels_test_cr.json, labels_test_fl.json, labels_test_fr.json, labels_test_free.json
    │── test_bgaug
    │   └── labels.json
    │── test_mpaug
    │   └── labels.json
    └── test_mpreal
        └── labels.json
```

The 'labels' folder contains all the label files.

```
    predictions
    ├── val
    │   ├── yolo_results.json
    │   ├── openpose_results.json
    │   ├── a2j_results.json
    │   └── pop_results.json
    ├── test_bgaug
    │   ├── yolo_results.json
    │   ├── openpose_results.json
    │   ├── a2j_results.json
    │   └── pop_results.json
    ├── test_mpaug
    │   ├── yolo_results.json
    │   ├── openpose_results.json
    │   ├── a2j_results.json
    │   └── pop_results.json
    └── test_mpreal
        ├── yolo_results.json
        ├── openpose_results.json
        ├── a2j_results.json
        └── pop_results.json
  ```

The 'prediction' folder contains the prediction results for four considered methods on the four testing set.

## Evaluation

The prediction result from each considered method can be compared to the ground-truth labels by using

main_evaluate_mp_human_3D.py
    
Specifically, our evaluation consider both best-match PCK and mAP metrics to evaluate a method from different aspects. 
The results of the considering methods on the four testing set are shown in the Table below:

<img src="figs/evaluation_results.png" width="400" />
 
 Table 2: Evaluation on MP-3DHP dataset. Competing methods are evaluated on four different testing sets. For each test set, the best method is marked in bold black while the second best method is marked in blue.

## Third Party Methods

FYI, the training and evaluation scripts for the third party methods (except our own method) are included in 'third_party_methods' folder to demonstrate the usage of the released dataset. You are expected to repeat the results of yolo-pose+, open-pose+(rtpose), and yolo-A2J as reported in our paper.

## Visualization

Each of the ground-truth set can be visualized by using

main_visualize_gt.py
    
The raw data folder and the label file needs to be set properly in the code. In the visualization, the 2D skeletons will
be displayed on depth images. If the visualizing set belongs to the training, the segmentation mask will also be 
visualized as the red region superimposed on the depth image. We did not provide visualization for 3D skeletons.

Each of the prediction results can be visualized by using

main_visualize_pred.py
    
The data folder and the prediciton result file needs to be set properly in the code.

Visualization of the predicated methods against the ground-truth are visualized on a set of most challenging examples 
from the testing set.

<img src="figs/vis_results_challenge.png" width="700" />
 
 Figure 3: Visual comparison of competing methods on challenging cases.

## Video demos

A few video clips are included below showing visual comparison between few baseline methods and our PoP-Net. You are highly welcome to take the chalenge to beat the state-of-the-art methods!

https://user-images.githubusercontent.com/13290379/137015881-7badaeae-33ec-4ddf-96da-7b140a705f40.mp4

https://user-images.githubusercontent.com/13290379/137015952-a41044c2-f517-4dfd-bdad-2b198a8f9e1a.mp4

https://user-images.githubusercontent.com/13290379/137015206-1406a4e4-4393-49c7-b487-8b4fd029db0b.mp4

## Citation
Please cite the paper in your publications if it helps your research:

@InProceedings{Guo_2022_WACV,
    author    = {Guo, Yuliang and Li, Zhong and Li, Zekun and Du, Xiangyu and Quan, Shuxue and Xu, Yi},
    title     = {PoP-Net: Pose Over Parts Network for Multi-Person 3D Pose Estimation From a Depth Image},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2022},
    pages     = {1240-1249}
    }

PoP-Net

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