  In this paper, we tackle the problem of Egocentric Human-Object Interaction
(EHOI) detection in an industrial setting. To overcome the lack of public
datasets in this context, we propose a pipeline and a tool for generating
synthetic images of EHOIs paired with several annotations and data signals
(e.g., depth maps or segmentation masks). Using the proposed pipeline, we
present EgoISM-HOI a new multimodal dataset composed of synthetic EHOI images
in an industrial environment with rich annotations of hands and objects. To
demonstrate the utility and effectiveness of synthetic EHOI data produced by
the proposed tool, we designed a new method that predicts and combines
different multimodal signals to detect EHOIs in RGB images. Our study shows
that exploiting synthetic data to pre-train the proposed method significantly
improves performance when tested on real-world data. Moreover, to fully
understand the usefulness of our method, we conducted an in-depth analysis in
which we compared and highlighted the superiority of the proposed approach over
different state-of-the-art class-agnostic methods. To support research in this
field, we publicly release the datasets, source code, and pre-trained models at
https://iplab.dmi.unict.it/egoism-hoi.
