  In this study, we investigate the effectiveness of synthetic data in
enhancing egocentric hand-object interaction detection. Via extensive
experiments and comparative analyses on three egocentric datasets, VISOR,
EgoHOS, and ENIGMA-51, our findings reveal how to exploit synthetic data for
the HOI detection task when real labeled data are scarce or unavailable.
Specifically, by leveraging only 10% of real labeled data, we achieve
improvements in Overall AP compared to baselines trained exclusively on real
data of: +5.67% on EPIC-KITCHENS VISOR, +8.24% on EgoHOS, and +11.69% on
ENIGMA-51. Our analysis is supported by a novel data generation pipeline and
the newly introduced HOI-Synth benchmark which augments existing datasets with
synthetic images of hand-object interactions automatically labeled with
hand-object contact states, bounding boxes, and pixel-wise segmentation masks.
Data, code, and data generation tools to support future research are released
at: https://fpv-iplab.github.io/HOI-Synth/.
