Panel2Page: Structured Visual Narrative Generation with Omni-Panel Alignment

Structured visual generation requires models to satisfy hierarchical constraints beyond global text-image alignment, including global composition, local semantic correspondence, and cross-panel relational consistency. We study this challenge through text-to-comic page generation, where a model directly synthesizes a complete comic page from a structured prompt specifying layouts, ordered events, recurring characters, textual elements, and cross-panel relations. We propose Panel2Page, a two-stage framework that improves structured correspondence by separating global composition learning from hierarchical semantic alignment while preserving single-pass generation. Page-Level Supervised Fine-Tuning first learns a global structural prior from complete prompt-page pairs, capturing layout organization, reading order, and page-level composition. We then introduce Omni-Panel Alignment (OPA), a training-time strategy that regularizes unified page predictions using detached localized predictions constructed from complementary text-aware, panel-event, and cross-panel views, without introducing additional inference modules. To enable structured supervision, we construct a hierarchical dataset of approximately 20,000 manually verified prompt-page pairs and a benchmark of 320 structured prompts covering diverse layouts and narratives. Extensive experiments demonstrate that Panel2Page achieves the best overall performance among the evaluated full-page and panel-wise generation methods, showing the complementary benefits of global structural learning and hierarchical semantic alignment.