https://github.com/arthur151/relative_human
Relative Human dataset, CVPR 2022
https://github.com/arthur151/relative_human
depth human monocular
Last synced: about 1 month ago
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Relative Human dataset, CVPR 2022
- Host: GitHub
- URL: https://github.com/arthur151/relative_human
- Owner: Arthur151
- Created: 2022-03-26T18:39:19.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2025-03-22T01:11:33.000Z (3 months ago)
- Last Synced: 2025-04-01T18:50:16.205Z (3 months ago)
- Topics: depth, human, monocular
- Language: Python
- Homepage:
- Size: 2.24 MB
- Stars: 141
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
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](https://arxiv.org/abs/2112.08274).
**[[Project Page]](https://arthur151.github.io/BEV/BEV.html) [[Video]](https://youtu.be/Q62fj_6AxRI) [[BEV Code]](https://github.com/Arthur151/ROMP)**
## Download
[[Google drive]](https://drive.google.com/drive/folders/1NR3sOJARATaLaV45lXg2be8jarC9gxbS?usp=sharing)
[[Baidu drive]](https://pan.baidu.com/s/12z2rNU-Sex-LvS7AaV_Mfg?pwd=r3vh)
## Leaderboard
See [Leaderboard](https://paperswithcode.com/sota/3d-depth-estimation-on-relative-human).
## 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](demo.py), and [evaluation](RH_evaluation/evaluation.py).
To run the demo code, please download the data and set the dataset_dir in [demo code](demo.py).
To use it for training, please refer to [BEV](https://github.com/Arthur151/ROMP) for details.
## Re-implementation
To re-implement RH results (in Tab. 1 of BEV paper), please first download the predictions from [here](https://github.com/Arthur151/Relative_Human/releases/download/Predictions/all_results.zip), 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.
```bibtex
@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}
}
```