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arxiv 2504.12157 v3 pith:MBMALURU submitted 2025-04-16 cs.CV

FocusedAD: Character-centric Movie Audio Description

classification cs.CV
keywords characterfocusedadmovieaudiomodulecharacter-centriccharactersdescription
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Movie Audio Description (AD) aims to narrate visual content during dialogue-free segments, particularly benefiting blind and visually impaired (BVI) audiences. Compared with general video captioning, AD demands plot-relevant narration with explicit character name references, posing unique challenges in movie understanding.To identify active main characters and focus on storyline-relevant regions, we propose FocusedAD, a novel framework that delivers character-centric movie audio descriptions. It includes: (i) a Character Perception Module(CPM) for tracking character regions and linking them to names; (ii) a Dynamic Prior Module(DPM) that injects contextual cues from prior ADs and subtitles via learnable soft prompts; and (iii) a Focused Caption Module(FCM) that generates narrations enriched with plot-relevant details and named characters. To overcome limitations in character identification, we also introduce an automated pipeline for building character query banks. FocusedAD achieves state-of-the-art performance on multiple benchmarks, including strong zero-shot results on MAD-eval-Named and our newly proposed Cinepile-AD dataset. Code and data will be released at https://github.com/Thorin215/FocusedAD .

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Cited by 1 Pith paper

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  1. READ More than What You See: Reinforcement Learning for Accurate and Coherent Audio Description Generations

    cs.CV 2026-06 unverdicted novelty 6.0

    READ is the first reinforcement-learning framework for training audio-description generators, using sequence-level rewards for reference match, length, format, and context-aware coherence.