DAPE：面向扩散模型一致视频编辑的双阶段参数高效微调
DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models
AI总结：
本文提出两阶段参数高效微调框架DAPE，通过归一化微调与视觉友好适配器提升视频编辑的时间一致性和视觉质量，并构建含232个视频的新基准，实验显示其优于现有方法。
摘要：
基于扩散模型的视频生成是一项具有挑战性的多模态任务，而视频编辑正成为该领域的关键方向。近期的视频编辑方法主要分为两类：需要训练的方法和无需训练的方法。基于训练的方法会产生高昂的计算成本，而无需训练的替代方案往往性能欠佳。为解决这些局限，我们提出 DAPE，这是一个高质量且具有成本效益的两阶段参数高效微调（PEFT）视频编辑框架。在第一阶段，我们设计了一种高效的归一化微调方法，以增强生成视频的时间一致性。第二阶段引入视觉友好适配器，以提升视觉质量。此外，我们指出现有基准存在关键缺陷，包括类别多样性有限、对象分布不均衡以及帧数不一致。为缓解这些问题，我们构建了一个大型数据集基准，包含232个带有丰富标注的视频和6个编辑提示，能够对先进方法进行客观且全面的评估。在现有数据集（BalanceCC、LOVEU-TGVE、RAVE）和我们提出的基准上进行的大量实验表明，DAPE显著提升了时间连贯性和文本-视频对齐效果，同时优于以往的最先进方法。
英文摘要：
Video generation based on diffusion models presents a challenging multimodal task, with video editing emerging as a pivotal direction in this field. Recent video editing approaches primarily fall into two categories: training-required and training-free methods. While training-based methods incur high computational costs, training-free alternatives often yield suboptimal performance. To address these limitations, we propose DAPE, a high-quality yet cost-effective two-stage parameter-efficient fine-tuning (PEFT) framework for video editing. In the first stage, we design an efficient norm-tuning method to enhance temporal consistency in generated videos. The second stage introduces a vision-friendly adapter to improve visual quality. Additionally, we identify critical shortcomings in existing benchmarks, including limited category diversity, imbalanced object distribution, and inconsistent frame counts. To mitigate these issues, we curate a large dataset benchmark comprising 232 videos with rich annotations and 6 editing prompts, enabling objective and comprehensive evaluation of advanced methods. Extensive experiments on existing datasets (BalanceCC, LOVEU-TGVE, RAVE) and our proposed benchmark demonstrate that DAPE significantly improves temporal coherence and text-video alignment while outperforming previous state-of-the-art approaches.
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