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 c
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- PossiblePossibly related (embedding) · 54%VioletVision-3B →
- PossiblePossibly related (embedding) · 51%linyqh/NarratoAI →
- PossiblePossibly related (embedding) · 50%worldwonderer/video-recap-skills →
- PossiblePossibly related (embedding) · 49%modelscope/FunClip →
- PossiblePossibly related (embedding) · 48%HKUSTDial/DataMagic →
- LinkedLinked via arxiv author · 85%Seung Hyun Hahm →
“StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description”
- LinkedLinked via arxiv author · 85%Minh T. Dinh →
“StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description”
- LinkedLinked via arxiv author · 85%SouYoung Jin →
“StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description”