Captain Safari：具有位姿对齐3D记忆的世界引擎
Captain Safari: A World Engine with Pose-Aligned 3D Memory
提出位姿对齐的持久世界记忆与检索器，为长时程、6-DoF可控视频生成提供可复用机制；OpenSafari数据集与几何/运动学验证流程可用于评估相机轨迹跟随与3D一致性。
原文依据 we introduce Captain Safari, a pose-conditioned world engine that generates videos by retrieving from a persistent world memory
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论世界引擎旨在合成支持用户在相机运动控制下对场景进行交互式探索的长时程、3D一致的视频。然而，现有系统在剧烈的6自由度轨迹和复杂户外布局下表现不佳：它们会丧失长程几何一致性、偏离目标路径，或退化为过于保守的运动。为此，我们提出Captain Safari，一种通过从持久世界记忆中检索来生成视频的位姿条件世界引擎。给定相机路径，我们的方法维护动态局部记忆，并使用检索器获取位姿对齐的世界令牌，进而沿轨迹条件化视频生成。该设计使模型能够保持稳定的3D结构，同时准确执行具有挑战性的相机运动。为评估该设定，我们构建了OpenSafari，一个全新的野外第一人称视角数据集，包含经过验证的相机轨迹的高动态无人机视频，通过多阶段几何与运动学验证流程构建。在视频质量、3D一致性和轨迹跟随方面，Captain Safari显著优于最优的相机控制生成器。它将MEt3R从0.3703降至0.3690，将AUC@30从0.181提升至0.200，并且FVD显著低于所有相机控制基线。更重要的是，在一项50名参与者、5路的人体研究中，标注者在五个匿名模型中选择最佳结果，67.6%的偏好支持我们的方法，且在所有维度上均如此。我们的结果表明，位姿条件的世界记忆是长时程、可控视频生成的有力机制，并提供OpenSafari作为未来世界引擎研究的具有挑战性的新基准。
英文原文摘要 1735 字符
World engines aim to synthesize long, 3D-consistent videos that support interactive exploration of a scene under user-controlled camera motion. However, existing systems struggle under aggressive 6-DoF trajectories and complex outdoor layouts: they lose long-range geometric coherence, deviate from the target path, or collapse into overly conservative motion. To this end, we introduce Captain Safari, a pose-conditioned world engine that generates videos by retrieving from a persistent world memory. Given a camera path, our method maintains a dynamic local memory and uses a retriever to fetch pose-aligned world tokens, which then condition video generation along the trajectory. This design enables the model to maintain stable 3D structure while accurately executing challenging camera maneuvers. To evaluate this setting, we curate OpenSafari, a new in-the-wild FPV dataset containing high-dynamic drone videos with verified camera trajectories, constructed through a multi-stage geometric and kinematic validation pipeline. Across video quality, 3D consistency, and trajectory following, Captain Safari substantially outperforms state-of-the-art camera-controlled generators. It reduces MEt3R from 0.3703 to 0.3690, improves AUC@30 from 0.181 to 0.200, and yields substantially lower FVD than all camera-controlled baselines. More importantly, in a 50-participant, 5-way human study where annotators select the best result among five anonymized models, 67.6% of preferences favor our method across all axes. Our results demonstrate that pose-conditioned world memory is a powerful mechanism for long-horizon, controllable video generation and provide OpenSafari as a challenging new benchmark for future world-engine research.