The paper introduces Cue2Narrate, a two‑stage pipeline that jointly predicts what visual events to narrate and when to insert the narration in long, untrimmed movie clips. It uses a dual‑head audio‑visual localizer to identify visual cue and narration windows, followed by a LoRA‑adapted vision‑language model that generates concise audio descriptions, trained with a Description Ranking Loss. The authors also present the LongLSMDC benchmark, comprising up to 8‑minute clips, and show that Cue2Narrate outperforms video‑only and audio‑only baselines by 5–12 points in average mAP and improves AD generation over fine‑tuned base VLMs.
By Akshita Gupta, Aditya Arora, Federico Tombari, Marcus Rohrbach, Anna Rohrbach
Audio Description (AD) provides spoken narration of visual events during dialogue gaps, making movies accessible to visually impaired audiences. The problem requires determining both what (which visua...
arXiv:2607. 11798v1 Announce Type: cross Abstract: 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.
By Seung Hyun Hahm, Minh T. Dinh, SouYoung Jin
The paper introduces MTG-AD, a 36‑hour dataset of professionally annotated inline audio descriptions for videos, created by blind and sighted professionals. It also presents MTG‑AD‑Eval, a reference‑free evaluation framework that measures how well generated descriptions fit within a video’s natural silences, providing fine‑grained metrics aligned with expert guidelines. Experiments show that while large multimodal language models can reduce speech overlaps, gaps remain in pacing, and users find the model‑generated descriptions highly beneficial for comprehension.
By Subhashini Venugopalan, Jenny Tan, Taylor Roper, Jimmy Tobin, Anton Kast, Alicia Martin, Sam Sepah, Amy Pavel
arXiv:2604. 25220v2 Announce Type: replace Abstract: Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communication.
By Ridwan Mahbub, Syem Aziz, Mizanur Rahman, Mahir Ahmed, Shadikur Rahman, Shafiq Joty, Enamul Hoque
Narrating the visual component of movies is known as audio description. It is a narrative technique designed to enable blind and visually impaired individuals to follow the story.
The paper introduces Temporal Context Routing (TCR), a method that aligns script timing with the shared temporal axis used for audio and video generation. By routing prompt guidance to specific temporal positions in both modalities, TCR dramatically improves shot boundary accuracy and dialogue timing on a set of test scripts, while preserving visual quality and audio‑visual sync. A user study confirms that participants prefer TCR across multiple evaluation dimensions.
By Yichen Liu, Quanwei Zhang, Haozhe Wang, Donghao Zhou, Xiaojie Li, Yang Shi, Jiaming Liu, Ruihua Huang, Yingtian Zou, Daquan Zhou
The paper introduces Temporally-Grounded Language Generation (TGLG), a benchmark that tests vision‑language models on their ability to produce semantically accurate and temporally precise utterances in real‑time settings. It identifies perceptual updating and contingency awareness as key capabilities, curates datasets from sports broadcasting and egocentric interactions, and proposes the TRACE metric to jointly evaluate semantic similarity and temporal alignment. The authors also present VLM‑TSI, a model that interleaves visual and linguistic tokens in a time‑synchronized manner, achieving better performance than a strong baseline yet still showing modest overall results, underscoring the challenge of real‑time VLMs.
By Keunwoo Peter Yu, Joyce Chai
arXiv:2610.32042v1 Announce Type: new
Abstract: Cross-lingual dubbing aims to convert source speech into target-language speech that remains synchronized with the original video. The key to this task...
By Jeongsoo Choi, Ji-Hoon Kim, Junseok Ahn, Joon Son Chung
arXiv:2608.28699v1 Announce Type: new
Abstract: Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine...
By Dong-Hee Kim, Seonwoo Choi, Changbeen Kim, Jungmyung Wi, Juyeon Ko, Youngju Choi, Il Hyeon Mun, Hyunwoo J. Kim, Donghyun Kim
The paper reviews recent work on natural language generation from visual events, emphasizing that tasks involving sequences of images or video frames are fundamentally about modeling complex relationships between evolving visual events and the language used to describe them. It surveys five related tasks, examines common modeling and evaluation methods, and highlights shared challenges across these tasks. The authors then outline key open questions and suggest future research directions to address these challenges.
By Aditya K Surikuchi, Raquel Fern\'andez, Sandro Pezzelle
Video-HolmesV2 is a new benchmark that tests multimodal large language models on their ability to reason with spatio‑temporal audio‑visual evidence in long videos. It requires models to justify answers with precise evidence, uses a multi‑model cross‑verification pipeline and a spatio‑temporal evidence‑aware metric, and introduces an audio‑text guided token compression framework to reduce long‑context noise. In evaluations, even strong proprietary models score below 60% while the proposed approach outperforms comparable open‑source omni‑models.
By Zhaoyang Wei, Zipeng Wang, Yushe Cao, Chenhui Qiang, Shuaibing Cheng, Xuesong Yang, Sen Nie, Bowen Jiang, Wenchao Ding, Yanchao Hao, Zheng Wei, Xuehui Yu, Zhenjun Han