这个框架不用训练就能让AI给长视频生成连贯的旁白，特别适合帮助视障观众理解剧情。
StoryTeller 是一种无需训练的框架，用于长格式音频描述（AD），可保留角色、事件和故事上下文。它维护经过验证的叙事记忆，跨场景传递故事相关信息，无需字幕、脚本或微调。在标准 AD 基准和多种长视频上，StoryTeller 在自动评估、QA 评估和人工评估中一致提升了叙事连贯性和事实准确性。作者还引入了 StoryAD-QA 基准来测试生成描述的故事理解能力。
StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description
Long-form audio description (AD) requires more than describing visible actions: it must preserve characters, events, relationships, and story context across scenes so that blind and low-vision (BLV) audiences can follow a film. Modern video-language models (VLMs) are effective on short clips, but they often treat each moment independently, producing descriptions that miss who characters are, why events matter, and how the current scene connects to earlier narrative context. We propose StoryTeller, a training-free framework for story-aware long-form AD. Instead of relying only on local visual cues, StoryTeller maintains a verified narrative memory that carries forward story-relevant information across scenes, enabling later descriptions to remain coherent, grounded, and contextually informative. Given only raw video and a movie title, StoryTeller can optionally retrieve public movie metadata to resolve names and story context, while accepting only facts that are supported by the video through semantic filtering and VLM verification. The method requires no subtitles, scripts, AD transcripts, aligned captions, character banks, precomputed face identities, or task-specific fine-tuning. To evaluate whether generated AD preserves narrative information, we introduce StoryAD-QA, a question-answering benchmark that tests whether a language model can answer story-context questions using only the generated descriptions. Experiments on standard AD benchmarks and diverse long-form videos show that StoryTeller consistently improves narrative coherence, factual grounding, and story comprehension over strong baselines in automatic, QA-based, and human evaluations.