VANTAGE: Temporally Structured Predictive Uncertainty for Probabilistic Egocentric Gaze Anticipation

Egocentric gaze anticipation is inherently uncertain, yet existing methods predominantly predict a single deterministic future sequence, leaving plausible future variations unmodeled, the structure of predictive uncertainty uncharacterized, and its reliability information unused. Our key insights are that (i) the conditional variability of future gaze is temporally structured: the uncertainty varies along the anticipation horizon, and (ii) predictive uncertainty manifests in trajectory-level and gaze heatmap-level forms. We therefore reformulate the task as conditional probabilistic forecasting and propose **VANTAGE**, a temporally structured variational framework that unifies latent modeling and two-level uncertainty estimation over a shared interval-based horizon decomposition, with the estimated uncertainty directly guiding temporal attention during decoding. Experiments on three datasets demonstrate the effectiveness of the proposed temporally structured modeling and uncertainty estimation, while revealing different roles of the two forms. These results support predictive uncertainty as both a measure of prediction reliability and a signal for temporal reasoning.