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English(EN) Clips2Story: Training-Free Video Storyboarding and Editing Using Multimodal Retrieval-Embedded Generation

新的LLM管道从无序片段创建视频

研究人员开发了Clips2Story,这是一个新颖的管道,它使用一种无需训练、基于LLM的方法,从一系列不同的片段创建视频故事板和编辑视频。该系统首先从视频片段中提取多模态描述符,并使用关键字匹配检索最相关的描述符。然后将这些描述符输入到一个大型语言模型中,该模型将它们排列成一个可编辑的故事板,并配有旁白。最后,将选定的片段和旁白视觉效果组合起来,构成最终视频,证明了从无序素材中创建连贯叙事的效果。 AI

影响 利用LLM从无组织的素材中创建连贯的视频叙事。

排序理由 该条目描述了一篇发表在arXiv上的研究论文,详细介绍了一种新的视频编辑方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的LLM管道从无序片段创建视频

本文如何被排名

Signal score
26 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
该条目描述了一篇发表在arXiv上的研究论文,详细介绍了一种新的视频编辑方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junnuo Wang, Weihan Xu, Hao-Wen Dong ·

    Clips2Story:使用多模态检索嵌入生成进行无训练视频故事板制作和编辑

    arXiv:2611.05992v1 Announce Type: cross Abstract: Video editors often work with large collections of fragmented footage and must select and organize relevant clips into a coherent story. However, many existing video editing and summarization methods assume a temporally ordered so…