The official code for ICCV 2023 paper "Reconstructing Groups of People with Hypergraph Relational Reasoning"
Buzhen Huang, Jingyi Ju, Zhihao Li, Yangang Wang
[Project] [Paper]
Create conda environment and install dependencies.
conda create -n grouprec python=3.8
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu111 # install pytorch
pip install -r requirements.txt
Step1:
Download the official SMPL model from SMPLify website and put it in data/SMPL_NEUTRAL.pkl
.
Step2:
Download trained models from Baidu Netdisk and put them in data
.
Step3:
Run demo for 3D pose estimation.
python demo.py --config cfg_files/demo.yaml
Run demo for SMPL estimation.
python demo.py --config cfg_files/demo_smpl.yaml
We provide pseudo annotations for Panda dataset (Detection and MOT). You may also need to download image files from their official websites.
[Annotations] [Detection Image] [MOT Image]
- Demo code for pose estimation
- Demo code for SMPL estimation
- Training code release
If you find this code useful for your research, please consider citing the paper.
@inproceedings{grouprec,
title={Reconstructing Groups of People with Hypergraph Relational Reasoning},
author={Huang, Buzhen and Ju, Jingyi and Li, Zhihao and Wang, Yangang},
booktitle={ICCV},
year={2023},
}
Some of the code are based on the following works. We gratefully appreciate the impact it has on our work.
CLIFF
ByteTrack
LoCO
YOLOX