MAKIMA：基于掩码引导注意力调制的免调优多属性开放域视频编辑
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation
AI总结：
本文提出免调优多属性视频编辑框架MAKIMA，通过掩码引导注意力调制和一致特征传播，在保持视频结构与时间一致性的同时实现精确的多属性开放域编辑。
摘要：
基于扩散的文本到图像（T2I）模型在全局视频编辑任务中已展现出显著成果。然而，这些方法主要关注全局视频修改，实现期望的特定属性变化仍然具有挑战性，尤其是在视频中的多属性编辑（MAE）任务上。当前的视频编辑方法要么需要大量微调，要么依赖额外网络（如ControlNet）来建模多目标外观，但仍处于早期阶段，仅能提供粗粒度的多属性编辑方案。本文提出MAKIMA，一个建立在预训练T2I模型之上的免调优多属性编辑框架，用于开放域视频编辑。我们的方法通过在去噪过程中融合反演过程得到的注意力图和特征来保持视频结构和外观信息。为实现对多个属性的精确编辑，我们引入掩码引导的注意力调制，在自注意力和交叉注意力层中增强空间对应token之间的相关性，并抑制跨属性干扰。为平衡视频帧生成质量与效率，我们实现了一致的特征传播机制，通过编辑关键帧并将其特征传播到整个序列来生成帧序列。大量实验表明，MAKIMA在开放域多属性视频编辑任务中优于现有基线方法，在编辑准确性和时间一致性方面均取得了优越结果，同时保持了计算效率。
英文摘要：
Diffusion-based text-to-image (T2I) models have demonstrated remarkable results in global video editing tasks. However, their focus is primarily on global video modifications, and achieving desired attribute-specific changes remains a challenging task, specifically in multi-attribute editing (MAE) in video. Contemporary video editing approaches either require extensive fine-tuning or rely on additional networks (such as ControlNet) for modeling multi-object appearances, yet they remain in their infancy, offering only coarse-grained MAE solutions. In this paper, we present MAKIMA, a tuning-free MAE framework built upon pretrained T2I models for open-domain video editing. Our approach preserves video structure and appearance information by incorporating attention maps and features from the inversion process during denoising. To facilitate precise editing of multiple attributes, we introduce mask-guided attention modulation, enhancing correlations between spatially corresponding tokens and suppressing cross-attribute interference in both self-attention and cross-attention layers. To balance video frame generation quality and efficiency, we implement consistent feature propagation, which generates frame sequences by editing keyframes and propagating their features throughout the sequence. Extensive experiments demonstrate that MAKIMA outperforms existing baselines in open-domain multi-attribute video editing tasks, achieving superior results in both editing accuracy and temporal consistency while maintaining computational efficiency.
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。
