Retrieve What’s Missing: Coverage-Maximizing Retrieval for Consistent Long Video Generation

Maintaining long-term geometric consistency remains a key challenge in long-horizon autoregressive video generation. Recent memory-augmented generative models address this issue by retrieving historical frames beyond the limited temporal context, but their effectiveness depends on two key design choices: what 3D-geometric evidence should represent past observations, and how memory frames should be selected from this evidence. Existing methods often rely on camera poses or field-of-view overlap, which are lightweight but too coarse to reason about pixel-wise visibility, or use explicit 3D reconstruction, which provides fine-grained evidence but is costly to maintain over long rollouts. We propose Coverage-Maximizing Retrieval-Augmented Generation (COVRAG), a depth-based memory retrieval framework that uses pretrained 3D priors to construct a target-view coverage map as lightweight 3D memory evidence. For frame selection, COVRAG maximizes residual coverage gain, iteratively retrieving frames that explain target-view regions not covered by the current context or previously selected memories. To improve scalability in long-video generation, we introduce a sliding-window depth caching for efficient geometry estimation. Experiments on RealEstate10K and DL3DV show that \modelname improves long-horizon geometric consistency while maintaining low latency compared to baselines.