Code for the ECCV'22 paper "Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos".
You can find all RGB-D frames and geometric annotations in the two links below. We list two ways to download datasets in case one of the services collapses.
Durham University Library and Collections: download.
OneDrive: download.
First please create an appropriate environment using conda:
conda env create -f environment.yml
conda activate vhoi
Please download the necessary data from the link below, and put the
downloaded data folder in this current directory (i.e. ./data/...
).
Link: data.
To train the model from scratch, edit the ./conf/config.yaml
file, and depending on the selected dataset and model, also
edit the associated model .yaml file in ./conf/models/
and the associated dataset .yaml file in ./conf/data/
. After
editing the files, just run python train.py
.
Examples on MPHOI-72: when you get pre-trained models for all subject groups, you can get the cross-validation result by python -W ignore predict.py --pretrained_model_dir ./outputs/mphoi/2G-GCN/hs512_e40_bs8_lr0.0001_0.1_Subject14 --cross_validate
.
If you use our code or data, please cite:
@inproceedings{qiao2022geometric,
title={Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos},
author={Qiao, Tanqiu and Men, Qianhui and Li, Frederick W. B. and Kubotani, Yoshiki and Morishima, Shigeo and Shum, Hubert P. H.},
booktitle={European Conference on Computer Vision (ECCV)},
year={2022}
}