Ring Forcing：邁向自迴歸影片擴散的精確長期記憶

摘要

將影片生成擴展至長時間持續時，揭露了一個關鍵瓶頸：現有模型缺乏穩健的長期記憶。此缺陷可從兩個關鍵面向加以探討：物體恆存性，即物體重新出現時能精確重現其外觀的能力；以及記憶容量，即處理超長上下文並運用遙遠歷史資訊的能力。穩健的長期記憶需要兩者兼備：僅有物體恆存性而缺乏足夠的上下文處理，會限制時間範圍；反之，僅有長上下文而缺乏恆存性，則無法維持物體身分的一致性。為了解決此問題，我們提出 Ring Forcing，這是一個自迴歸影片擴散框架，旨在穩健地建構並精確運用長期記憶。我們的環狀結構訓練策略強制從遙遠歷史中檢索，有效調和了嚴格遵循歷史與生成多樣性之間的取捨。為了擴展記憶容量，我們引入了壓縮與時間步組合策略。在固定序列長度限制下，此方法將有效歷史跨度延伸至長達數分鐘的持續時間，並對整個歷史達成全面的感受野。此外，我們提出一個稀疏 RoPE 機制，以在充分利用預訓練先驗的同時，實現靈活且可擴展的記憶適應。大量實驗顯示，Ring Forcing 在數分鐘的連貫性與物體恆存性上表現卓越，顯著優於目前最先進的方法。

English

Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied along two critical aspects: object permanence, the ability to precisely reproduce the appearance of objects upon re-entry; and memory capacity, the ability to process ultra-long context and use information from distant history. Robust long-term memory requires both: object permanence without sufficient context handling limits the temporal scope, while long context length without permanence fails to maintain identity. To address this, we present Ring Forcing, an autoregressive video diffusion framework designed to robustly construct and precisely utilize long-term memory. Our ring-structured training strategy enforces retrieval from distant history, effectively reconciling the trade-off between strict historical adherence and generative diversity. To expand memory capacity, we introduce a compression and timestep composition strategy. Under fixed sequence length constraints, this method extends the effective historical span to minutes-long durations and achieves a comprehensive receptive field over the entire history. Furthermore, we present a sparse RoPE mechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors. Extensive experiments demonstrate that Ring Forcing achieves superior minutes-long coherence and object permanence, significantly outperforming state-of-the-art methods.