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Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable computational model of social engagement inspired by psychological theories of mutual visual attention. Rather than learning interaction patterns end-to-end, our framework explicitly models dyadic visual attention and aggregates these cues into interpretable measures of individual and group engagement. The resulting modular framework combines state-of-the-art head orientation estimation with lightweight geometric reasoning, producing explanations

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