Scene-Conditioned General Gaze Following in Static Images with Uncertainty-Preserving Geometry

Given a static image and a selected person, general gaze following predicts the person’s likely gaze target. Ambiguous head cues can leave several geometrically plausible targets. We develop a scene-conditioned localization framework that retains directional uncertainty instead of collapsing it into one gaze direction. Building on a fixed, previously published geometry-guided reference, Distribution-Guided Query Interaction (DGQI) refines direction–distance candidates using scene evidence, the Distribution-Conditioned Localization Decoder (DCLD) adjusts the reference heatmap through residual prediction, and Selective Teacher-Margin Ranking Preservation (STMRP) protects selected correct spatial rankings during training. On the GazeFollow official test set, the final model reaches a pooled/global AUC of 0.9190, an original-resolution per-image AUC of 0.9342, and a normalized distance error of 0.1289 compared to the published baseline values of 0.9066, 0.9214, and 0.1423, respectively. Within-reference controls distinguish module effects from cross-generation changes in the scene backbone. DCLD yields the clearest localization gains; DGQI contributes a smaller discrimination gain; and STMRP improves per-image AUC at a localization cost. This separation has revealed that discrimination and peak localization need not improve together. Zero-shot experiments on VideoAttentionTarget and a coverage-limited ChildPlay subset expose further domain sensitivity. These results motivate protocol-aware, multi-metric evaluation of gaze predictions.