StoryRelay: Reference-Addressable Autoregressive Visual Storytelling

Reference-conditioned visual storytelling must maintain subject identity and visual continuity across a sequence of shots while adapting to changing actions, viewpoints, and compositions. Generated history can improve cross-shot continuity, but it may also introduce irrelevant or incorrect visual state into later generations. We introduce **StoryRelay**, a reference-addressable autoregressive storytelling framework that jointly leverages persistent references and evolving visual history across shots. StoryRelay maintains story-level reference bindings and allows shot descriptions to address subject references and optional scene references through textual identifiers. For autoregressive story learning, *Autoregressive Story Modeling* (ASM) post-trains a unified multimodal model on interleaved reference-text-image stories, predicting each target keyframe from reference images and preceding ground-truth history. To strengthen identity resolution across multiple visual sources, *Shot-Aware Identity Grounding* (SIG) associates requested subjects with identity-specific evidence from references and historical appearances. At inference, a *feedback-controlled autoregressive rollout* performs history selection, generation, verification, and bounded shot-local correction within the same model. On ViStoryBench, StoryRelay improves cross-reference identity similarity from 0.45 to 0.52 and character-count matching from 65.57 to 68.46 over the original BAGEL.