Audio Descriptions (ADs) convey essential on-screen information, allowing visually impaired audiences to follow videos. To be effective, ADs must form a coherent sequence that helps listeners to visualise the unfolding scene, rather than describing isolated moments. However, most automatic methods generate each AD independently, often resulting in repetitive, incoherent descriptions. To address this, we propose a training-free method, CoherentAD, that first generates multiple candidate descriptions for each AD time interval, and then performs auto-regressive selection across the sequence to form a coherent and informative narrative. To evaluate AD sequences holistically, we introduce a sequence-level metric, StoryRecall, which measures how well the predicted ADs convey the ground truth narrative, alongside repetition metrics that capture the redundancy across consecutive AD outputs. Our method produces coherent AD sequences with enhanced narrative understanding, outperforming prior approaches that rely on independent generations.
@article{arxiv.2510.25440,
title = {More than a Moment: Towards Coherent Sequences of Audio Descriptions},
author = {Eshika Khandelwal and Junyu Xie and Tengda Han and Max Bain and Arsha Nagrani and Andrew Zisserman and Gül Varol and Makarand Tapaswi},
journal= {arXiv preprint arXiv:2510.25440},
year = {2025}
}