2 papers
cs.CV2026
EgoInteract: Synthetic Egocentric Videos Generation for Interaction Understanding and Anticipation
Rosario Leonardi, Francesco Ragusa, Daniele Materia +4
Collecting large-scale egocentric video datasets with dense spatial and temporal annotations is costly, slow, and often constrained by environmental biases, privacy constraints, an…
cs.CV2026
Leveraging Gaze and Set-of-Mark in VLLMs for Human-Object Interaction Anticipation from Egocentric Videos
Daniele Materia, Francesco Ragusa, Giovanni Maria Farinella
The ability to anticipate human-object interactions is highly desirable in an intelligent assistive system in order to guide users during daily life activities and understand their…