As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design.

Single-image 3D reconstruction of stylized side-face characters remains challenging because profile-view inputs contain severe self-occlusion, missing frontal geometry, and stylized appearance cues that differ from the assumptions of generic reconstruction models. Because the unseen facial geometry cannot be uniquely determined from a single profile-view input, this study focuses on generating plausible and visually consistent 3D completions rather than uniquely recovering the unobserved geometry. When CRM is directly applied to stylized profile inputs, the outputs often exhibit unstable facial completion, local mesh collapse, UV misalignment, texture discontinuity, and other reconstruction artifacts. Rather than introducing a new reconstruction backbone, this study first diagnoses the task-specific limitations of CRM in this setting. We identify eight characteristic failure modes that occur when CRM is directly applied to stylized profile inputs and use this diagnosis to guide a retraining-free inference-time intervention strategy. The proposed strategy combines reconstruction-compatible auxiliary-view generation with failure-mode-oriented CRM refinement, including candidate verification, adaptive facial cropping, geometric stabilization, local detail enhancement, normal correction, UV repair, and texture continuity improvement. Experiments on a rendered stylized-character dataset and a cross-style adaptation set show that the proposed intervention improves frontal-view plausibility, mesh usability, texture continuity, and rendered appearance compared with direct reconstruction baselines. The seven-configuration progressive ablation and parameter sensitivity analyses further support the complementary role of the main intervention stages and the stability of the selected settings. These findings suggest that systematic failure-mode diagnosis, followed by task-specific inference-time intervention, provides a practical way to extend public image-to-3D models to stylized profile reconstruction scenarios, within the scope of the evaluated stylized-character datasets, while extreme viewpoints and highly abstract styles remain challenging.