From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding

TL;DR

This paper introduces HIVE, a multimodal, human-inspired framework for automatic video editing that improves coherence and engagement by understanding narrative context, character interactions, and scene structure, outperforming existing methods.

Contribution

The paper presents a novel multimodal narrative understanding framework for automatic video editing, incorporating character, dialogue, and scene analysis, along with a new dataset for benchmarking.

Findings

Outperforms existing automatic editing baselines

Significantly narrows quality gap with human editing

Effective in both general and advertisement video editing tasks

Abstract

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.