AnchorWeave: 通过检索的局部空间记忆实现世界一致的视频生成
AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
AI总结：
AnchorWeave通过检索局部空间记忆提升视频生成的长期场景一致性与视觉质量。
摘要：
在长时间范围内保持空间世界一致性仍然是相机可控视频生成的核心挑战。现有基于记忆的方法通常通过从历史中重建的几何结构渲染锚视频，将生成条件限定于全局重建的3D场景。然而，从多个视角重建全局3D场景不可避免地引入跨视角不一致，因为姿态和深度估计误差导致相同表面在不同视角下被重建到略微不同的3D位置。当这些不一致被融合时，它们会积累成噪声几何体，污染条件信号并降低生成质量。我们引入了AnchorWeave，一种记忆增强的视频生成框架，它用多个干净的局部几何记忆取代单一的不一致全局记忆，并学习解决它们的跨视角不一致。为此，AnchorWeave执行覆盖驱动的局部记忆检索，与目标轨迹对齐，并在生成过程中通过多锚点编织控制器整合所选的局部记忆。广泛的实验表明，AnchorWeave在保持高质量视觉效果的同时显著提高了长期场景一致性，消融和分析研究进一步验证了局部几何条件、多锚点控制和覆盖驱动检索的有效性。
英文摘要：
Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross-view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross-view inconsistencies. To this end, AnchorWeave performs coverage-driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi-anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi-anchor control, and coverage-driven retrieval.
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