Memory Forcing：面向 Minecraft 一致场景生成的时空记忆
Memory Forcing: Spatio-Temporal Memory for Consistent Scene Generation on Minecraft
AI总结：
针对 Minecraft 交互场景生成中新场景质量与重访空间一致性的权衡，提出结合几何索引空间记忆、混合训练与链式展开训练的 Memory Forcing 框架。
摘要：
自回归视频扩散模型已被证明对世界建模和交互式场景生成有效，Minecraft 游戏玩法是其代表性应用。为了忠实地模拟游戏，模型必须在探索新场景时生成自然内容，并在重访已探索区域时保持空间一致性。在有限计算预算下，它必须在有限上下文窗口内压缩并利用历史线索，这暴露出一种权衡：仅使用时间记忆缺乏长期空间一致性，而加入空间记忆虽能增强一致性，但当模型过度依赖不充分的空间上下文时，可能降低新场景生成质量。我们提出 Memory Forcing，这是一种将训练协议与几何索引空间记忆相结合的学习框架。Hybrid Training 呈现不同的游戏模式，引导模型在探索期间依赖时间记忆，并在重访时纳入空间记忆。Chained Forward Training 通过模型展开扩展自回归训练，其中链式预测产生更大的位姿变化，并鼓励依赖空间记忆以维持一致性。Point-to-Frame Retrieval 通过将当前可见点映射到其源帧来高效检索历史，而 Incremental 3D Reconstruction 维护并更新显式 3D 缓存。大量实验表明，Memory Forcing 在多样环境中实现了更优的长期空间一致性和生成质量，同时在扩展序列上保持计算效率。
英文摘要：
Autoregressive video diffusion models have proved effective for world modeling and interactive scene generation, with Minecraft gameplay as a representative application. To faithfully simulate play, a model must generate natural content while exploring new scenes and preserve spatial consistency when revisiting explored areas. Under limited computation budgets, it must compress and exploit historical cues within a finite context window, which exposes a trade-off: Temporal-only memory lacks long-term spatial consistency, whereas adding spatial memory strengthens consistency but may degrade new scene generation quality when the model over-relies on insufficient spatial context. We present Memory Forcing, a learning framework that pairs training protocols with a geometry-indexed spatial memory. Hybrid Training exposes distinct gameplay regimes, guiding the model to rely on temporal memory during exploration and incorporate spatial memory for revisits. Chained Forward Training extends autoregressive training with model rollouts, where chained predictions create larger pose variations and encourage reliance on spatial memory for maintaining consistency. Point-to-Frame Retrieval efficiently retrieves history by mapping currently visible points to their source frames, while Incremental 3D Reconstruction maintains and updates an explicit 3D cache. Extensive experiments demonstrate that Memory Forcing achieves superior long-term spatial consistency and generative quality across diverse environments, while maintaining computational efficiency for extended sequences.
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