  Visual interactivity understanding within visual scenes presents a
significant challenge in computer vision. Existing methods focus on complex
interactivities while leveraging a simple relationship model. These methods,
however, struggle with a diversity of appearance, situation, position,
interaction, and relation in videos. This limitation hinders the ability to
fully comprehend the interplay within the complex visual dynamics of subjects.
In this paper, we delve into interactivities understanding within visual
content by deriving scene graph representations from dense interactivities
among humans and objects. To achieve this goal, we first present a new dataset
containing Appearance-Situation-Position-Interaction-Relation predicates, named
ASPIRe, offering an extensive collection of videos marked by a wide range of
interactivities. Then, we propose a new approach named Hierarchical
Interlacement Graph (HIG), which leverages a unified layer and graph within a
hierarchical structure to provide deep insights into scene changes across five
distinct tasks. Our approach demonstrates superior performance to other methods
through extensive experiments conducted in various scenarios.
