Relative Human dataset, CVPR 2022

Related tags

Deep Learning human depth monocular
Overview

Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including:

  • Depth layers (DLs): relative depth relationship/ordering between all people in the image.
  • Age group classfication: adults, teenagers, kids, babies.
  • Others: Genders, Bounding box, 2D pose.

RH is introduced in CVPR 2022 paper Putting People in their Place: Monocular Regression of 3D People in Depth.

[Project Page] [Video] [BEV Code]

Download

[Google drive]
[Baidu drive]

Leaderboard

See Leaderboard.

Why do we need RH?

Existing 3D datasets are poor in diversity of age and multi-person scenories. In contrast, RH contains richer subjects with explicit age annotations in the wild. We hope that RH can promote relative research, such as monocular depth reasoning, baby / child pose estimation, and so on.

How to use it?

We provide a toolbox for data loading, visualization, and evaluation.

To run the demo code, please download the data and set the dataset_dir in demo code.

To use it for training, please refer to BEV for details.

Re-implementation

To re-implement RH results (in Tab. 1 of BEV paper), please first download the predictions from here, then

cd Relative_Human/
# BEV / ROMP / CRMH : set the path of downloaded results (.npz) in RH_evaluation/evaluation.py, then run
python -m RH_evaluation.evaluation

cd RH_evaluation/
# 3DMPPE: set the paths in eval_3DMPPE_RH_results.py and then run
python eval_3DMPPE_RH_results.py
# SMAP: set the paths in eval_SMAP_RH_results.py and then run
python eval_SMAP_RH_results.py

Citation

Please cite our paper if you use RH in your research.

@InProceedings{sun2022BEV,
author = {Sun, Yu and Liu, Wu and Bao, Qian and Fu, Yili and Mei, Tao and Black, Michael J},
title = {Putting People in their Place: Monocular Regression of {3D} People in Depth}, 
booktitle = {IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)}, 
year = {2022}
}
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