这篇论文提出一种新的动态视角合成方法，通过分开记忆更新和应用的频率，实现了实时处理，而且能记住长视频里的短暂遮挡区域。
该方法将记忆更新与记忆应用解耦，记忆更新周期进行、记忆应用逐帧完成，并采用跨视角注意力处理记忆状态与当前帧之间的形变。引入辅助Memory Loss强制模型内化场景历史，Memory Caching策略防止主动权重灾难性漂移。在动态人体运动场景上实现实时处理，SOTA性能，支持分钟级在线记忆。
Online Neural Space Time Memory for Dynamic Novel View Synthesis
Online novel view synthesis from multi-view streaming videos faces a fundamental trade-off: maintaining a persistent, long-horizon memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful memory mechanism, standard models mandate gradient-based memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy memory updates precludes real-time application and can lead to instability over long contexts. Given that memory updates are more demanding than memory application and video content is largely redundant, we propose to decouple the frequencies of these two processes. Our approach performs periodic memory updates while applying the memory on a per-frame basis, using cross-view attention to manage deformations between the prior memory state and the current frame. To lock in the historical context, we introduce two critical mechanisms: an auxiliary Memory Loss that forces persistent internalization of the scene, and a Memory Caching strategy that regularizes active weights against catastrophic drift. Our method demonstrates real-time, state-of-the-art performance on scenes with dynamic human motion as well as minute-scale online memorization.