从长视频到引人入胜的片段：一种具备多模态叙事理解的类人灵感视频编辑框架
From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding
AI总结：
针对现有自动视频编辑依赖文本线索、忽略视觉上下文导致输出不连贯的问题，提出融合多模态叙事理解的HIVE框架，通过场景分割拆分编辑子任务，还构建了DramaAD数据集，实验显示其性能优于基线，缩小了与人工编辑的质量差距。
摘要：
在线视频内容的快速增长，尤其是短视频平台上的内容，催生了对高效视频编辑技术日益增长的需求，这类技术可将长视频压缩为简洁且引人入胜的片段。现有的自动编辑方法主要依赖自动语音识别（ASR）转录文本的文本线索以及端到端片段选择，往往忽略了丰富的视觉上下文，导致输出内容缺乏连贯性。本文提出了一种类人灵感的自动视频编辑框架HIVE，该框架利用多模态叙事理解来解决这些局限性。我们的方法通过多模态大语言模型整合了角色提取、对话分析和叙事摘要功能，能够对视频内容形成整体理解。为进一步提升连贯性，我们应用了场景级分割，并将编辑过程分解为三个子任务：高光检测、开头/结尾选择以及无关内容删减。为推动该领域的研究，我们推出了DramaAD这一新型基准数据集，其中包含800多集短剧和500个专业编辑的广告片段。实验结果表明，我们的框架在通用编辑任务和面向广告的编辑任务中均始终优于现有基线方法，显著缩小了自动编辑视频与人工编辑视频之间的质量差距。
英文摘要：
The rapid growth of online video content, especially on short video platforms, has created a growing demand for efficient video editing techniques that can condense long-form videos into concise and engaging clips. Existing automatic editing methods predominantly rely on textual cues from ASR transcripts and end-to-end segment selection, often neglecting the rich visual context and leading to incoherent outputs. In this paper, we propose a human-inspired automatic video editing framework (HIVE) that leverages multimodal narrative understanding to address these limitations. Our approach incorporates character extraction, dialogue analysis, and narrative summarization through multimodal large language models, enabling a holistic understanding of the video content. To further enhance coherence, we apply scene-level segmentation and decompose the editing process into three subtasks: highlight detection, opening/ending selection, and pruning of irrelevant content. To facilitate research in this area, we introduce DramaAD, a novel benchmark dataset comprising over 800 short drama episodes and 500 professionally edited advertisement clips. Experimental results demonstrate that our framework consistently outperforms existing baselines across both general and advertisement-oriented editing tasks, significantly narrowing the quality gap between automatic and human-edited videos.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
