DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing
AI 摘要
DIRECT框架通过层级多智能体规划实现高质量视频混剪,优化视觉和听觉连贯性。
主要贡献
- 提出多模态连贯性满足问题(MMCSP),并设计DIRECT框架。
- 构建层级多智能体框架,包含Screenwriter, Director, Editor三个层级。
- 引入Mashup-Bench基准,包含视觉连贯性和听觉对齐的指标。
方法论
将视频混剪分解为MMCSP,通过层级多智能体架构,逐层优化视频的结构、意图和编辑细节。
原文摘要
Video mashup creation represents a complex video editing paradigm that recomposes existing footage to craft engaging audio-visual experiences, demanding intricate orchestration across semantic, visual, and auditory dimensions and multiple levels. However, existing automated editing frameworks often overlook the cross-level multimodal orchestration to achieve professional-grade fluidity, resulting in disjointed sequences with abrupt visual transitions and musical misalignment. To address this, we formulate video mashup creation as a Multimodal Coherency Satisfaction Problem (MMCSP) and propose the DIRECT framework. Simulating a professional production pipeline, our hierarchical multi-agent framework decomposes the challenge into three cascade levels: the Screenwriter for source-aware global structural anchoring, the Director for instantiating adaptive editing intent and guidance, and the Editor for intent-guided shot sequence editing with fine-grained optimization. We further introduce Mashup-Bench, a comprehensive benchmark with tailored metrics for visual continuity and auditory alignment. Extensive experiments demonstrate that DIRECT significantly outperforms state-of-the-art baselines in both objective metrics and human subjective evaluation. Project page and code: https://github.com/AK-DREAM/DIRECT