PermaVid：通过解耦的上下文记忆实现跨编辑过程的一致视频生成
PermaVid: Consistent Video Generation Across Edits via Disentangled Context Memory
摘要
在编辑过程中，连续的视频生成需要保持一致性：当编辑操作改变了场景的外观或布局时，后续的生成结果应该能够在不同的时间和视角下保持连贯性。然而，现有的内存设计在面临此类修改时难以维持长期的一致性，因为存储的上下文信息可能会变得过时或无效。为了解决这一问题，我们提出了PermaVid框架。该框架基于多模态上下文内存结构，能够将空间上下文分解为语义外观和几何结构两部分；同时，我们的系统还采用了一种能够适应编辑操作的记忆更新与检索策略，使得记忆中的内容能够随着后续观察结果而不断更新。具体来说，我们设计了两个互补的内存模块：一个RGB上下文内存，用于捕捉与外观相关的信息，同时隐式地编码几何信息；另一个深度上下文内存则专门用于保存与语义无关的几何结构信息。基于这一设计，我们提出了一个基于记忆引导的视频生成模型，该模型能够在来自不同模态上下文的信息下进行多模态特征融合。实验表明，我们的方法在编辑后仍能保持较强的语义和结构性一致性，其性能明显优于现有最先进的方法。
English Abstract
Consistent video generation under editing operations requires persistence: when edits modify scene appearance or layout, subsequent generations should remain coherent across time and viewpoints. However, existing memory designs struggle to maintain long-term consistency after such modifications, as stored contexts may become outdated or invalid. To address this, we propose PermaVid, a novel framework built upon a multi-modal context memory that disentangles spatial context into semantic appearance and geometric structure, together with an edit-aware memory update and retrieval strategy that keeps memory evolution aligned with subsequent observations. Specifically, we develop two complementary memory banks: an RGB context memory that captures appearance-aware observations while implicitly encoding geometry, and a depth context memory that preserves geometry-only structure disentangled from semantics. Building on this design, we introduce a memory-guided video generation model that performs multi-modal feature fusion under reference conditions drawn from mixed-modality memory contexts. Experiments demonstrate that our method maintains strong long-term semantic and structural consistency after edits, significantly outperforming state-of-the-art methods.