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]
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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}
}