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Multi-person Human-object Interaction Recognition in Videos

Code for the ECCV'22 paper "Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos".

MPHOI-72 RGB-D Dataset

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.

Environment Setup

First please create an appropriate environment using conda:

conda env create -f environment.yml

conda activate vhoi

Download Data

Please download the necessary data from the link below, and put the downloaded data folder in this current directory (i.e. ./data/...).

Link: data.

Train the Model

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.

Test the Model

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.

Citation

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}
}