StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description

Long-form audio description (AD) requires more than de-scribing visible actions: it must preserve characters, events, relationships,and story context across scenes so that blind and low-vision (BLV) audi-ences can follow a film. Modern video–language models (VLMs) are effec-tive on short clips, but they often treat each moment independently, pro-ducing descriptions that miss who characters are, why events matter, andhow the current scene connects to earlier narrative context. We proposeStoryTeller, a training-free framework for story-aware long-form AD. In-stead of relying only on local visual cues, StoryTeller maintains a verifiednarrative memory that carries forward story-relevant information acrossscenes, enabling later descriptions to remain coherent, grounded, andcontextually informative. Given only raw video and a movie title, Sto-ryTeller can optionally retrieve public movie metadata to resolve namesand story context, while accepting only facts that are supported by thevideo through semantic filtering and VLM verification. The method re-quires no subtitles, scripts, AD transcripts, aligned captions, characterbanks, precomputed face identities, or task-specific fine-tuning. To evalu-ate whether generated AD preserves narrative information, we introduceStoryAD-QA1 , a question-answering benchmark that tests whether alanguage model can answer story-context questions using only the gener-ated descriptions. Experiments on standard AD benchmarks and diverselong-form videos show that StoryTeller consistently improves narrativecoherence, factual grounding, and story comprehension over strong base-lines in automatic, QA-based, and human evaluations.