RECAP-Forcing：用於長視頻生成的內容外觀保留

摘要

長篇自回歸影片生成面臨一個根本的記憶挑戰：在有限的注意力視窗下，模型必須決定要從不斷擴展的歷史中保留哪些資訊。現有方法按時間順序組織記憶，保留最近的幀，同時壓縮或丟棄較舊的幀。我們反而提出 RECAP-Forcing，以外觀新穎性來組織記憶。長影片不僅僅是一系列幀，而是一個由主體、物體和場景組成的不斷演變的陣容，其身份必須隨時間保持一致。我們透過在內容首次變得可見的時刻，保留與新出現內容相關的 KV 快取——例如進入畫面的主體、去遮擋區域和新引入的場景——來組織記憶，優先考量新穎性而非時近性。記憶應隨新引入內容的數量擴展，而非影片長度。這種以外觀索引的記憶使長程一致性成為記憶結構的明確屬性。我們的框架在此單一原則下統一了兩種機制。在影片開始時，當所有可見內容皆屬新穎時，一個注意力匯聚點保留初始場景。隨著影片演進，基於光流的新穎性記憶庫透過選擇性地保留新揭示的內容來延伸相同原則。作為一種無需額外可學習參數的免訓練推論方法，RECAP-Forcing 在多個強基線模型上持續改善視覺品質與語意保真度，並優於現有的記憶方法。

English

Long autoregressive video generation faces a fundamental memory challenge: with a finite attention window, a model must decide which information from an ever-expanding history to retain. Existing methods organize memory temporally, preserving recent frames while compressing or discarding older ones. We instead propose RECAP-Forcing, organizing memory by appearance novelty. A long video is not merely a sequence of frames, but an evolving cast of subjects, objects, and scenes whose identities must remain consistent over time. We organize memory by retaining the KV cache associated with newly appearing content--such as entering subjects, disoccluded regions, and newly introduced scenes--at the moment it first becomes visible, prioritizing novelty over recency. Memory should scale with the amount of newly introduced content, rather than with video length. This appearance-indexed memory makes long-range consistency an explicit property of the memory structure. Our framework unifies two mechanisms under this single principle. At the beginning of a video, when all visible content is novel, an attention sink preserves the initial scene. As the video evolves, an optical-flow-based novelty bank extends the same principle by selectively retaining newly revealed content. As a training-free inference method with no additional learnable parameters, RECAP-Forcing consistently improves visual quality and semantic fidelity across multiple strong baselines and outperforms existing memory methods.