arXiv AI By Junnuo Wang, Weihan Xu, Hao-Wen Dong

Clips2Story: Training-Free Video Storyboarding and Editing Using Multimodal Retrieval-Embedded Generation

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Clips2Story is a training‑free, LLM‑based pipeline that lets users generate short videos from a pool of clips by specifying a keyword. It first extracts multimodal descriptors from each clip, retrieves the most relevant ones, and then uses an LLM to select, arrange, and narrate them into an editable storyboard. The final video is assembled by combining the chosen clips with visuals that match the narration. Evaluation on a synthetic dataset of shuffled clips from 100 long‑form videos shows that Clips2Story achieves better narrative coherence, keyword alignment, and realism than a strong baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 20

LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

arXiv:2604. 10024v2 Announce Type: replace-cross Abstract: Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded.

By Alkesh Patel, Melis Ozyildirim, Ying-Chang Cheng, Ganesh Nagarajan
arXiv AI
6d ago

MEV: A Multi-Event Video Dataset for Long-Take Generation

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