AutoCut：基于多模态离散化和可控生成的端到端广告视频编辑
AutoCut: End-to-end advertisement video editing based on multimodal discretization and controllable generation
AI总结：
本文提出AutoCut框架，通过多模态离散化和可控生成实现广告视频编辑，提升一致性和可控性，降低生产成本和迭代时间。
摘要：
短视频已成为数字广告的主要媒介，需要可扩展和高效的创作方法。然而，当前的工作流和AI工具仍然分散且模态特定，导致生产成本高和整体效率低。为此，我们提出了AutoCut，一种基于多模态离散化和可控编辑的端到端广告视频编辑框架。AutoCut使用专用编码器提取视频和音频特征，然后应用残差向量量化将它们离散化为与文本表示对齐的统一标记，构建共享的视频-音频-文本标记空间。基于基础模型，我们进一步开发了多模态大语言模型用于视频编辑，通过结合多模态对齐和监督微调，支持视频选择和排序、脚本生成和背景音乐选择等任务。最后，一个完整的生产流程将预测的标记序列转换为可部署的长视频输出。在真实世界广告数据集上的实验表明，AutoCut在降低生产成本和迭代时间的同时，显著提高了一致性和可控性，为可扩展的视频创作铺平了道路。
英文摘要：
Short-form videos have become a primary medium for digital advertising, requiring scalable and efficient content creation. However, current workflows and AI tools remain disjoint and modality-specific, leading to high production costs and low overall efficiency. To address this issue, we propose AutoCut, an end-to-end advertisement video editing framework based on multimodal discretization and controllable editing. AutoCut employs dedicated encoders to extract video and audio features, then applies residual vector quantization to discretize them into unified tokens aligned with textual representations, constructing a shared video-audio-text token space. Built upon a foundation model, we further develop a multimodal large language model for video editing through combined multimodal alignment and supervised fine-tuning, supporting tasks covering video selection and ordering, script generation, and background music selection within a unified editing framework. Finally, a complete production pipeline converts the predicted token sequences into deployable long video outputs. Experiments on real-world advertisement datasets show that AutoCut reduces production cost and iteration time while substantially improving consistency and controllability, paving the way for scalable video creation.
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。
