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Progress in human behavior modeling involves understanding both implicit,early-stage perceptual behavior such as human attention and explicit,later-stage behavior such as subjective ratings/preferences. Yet, most priorresearch has focused on modeling implicit and explicit human behavior inisolation. Can we build a unified model of human attention and preferencebehavior that reliably works across diverse types of visual content? Such amodel would enable predicting subjective feedback such as overall satisfactionor aesthetic quality ratings, along with the underlying human attention orinteraction heatmaps and viewing order, enabling designers and content-creationmodels to optimize their creation for human-centric improvements. In thispaper, we propose UniAR -- a unified model that predicts both implicit andexplicit human behavior across different types of visual content. UniARleverages a multimodal transformer, featuring distinct prediction heads foreach facet, and predicts attention heatmap, scanpath or viewing order, andsubjective rating/preference. We train UniAR on diverse public datasetsspanning natural images, web pages and graphic designs, and achieve leadingperformance on multiple benchmarks across different image domains and variousbehavior modeling tasks. Potential applications include providing instantfeedback on the effectiveness of UIs/digital designs/images, and serving as areward model to further optimize design/image creation.

UniAR: Unifying Human Attention and Response Prediction on Visual Content