Re-rendering an existing video from a novel camera viewpoint requires the output to follow the prescribed camera trajectory while preserving the appearance and dynamics of the original scene across every frame. Existing methods rely on per-frame pose embeddings, noisy point-cloud renderings, or implicit learned correspondences, none of which provides an explicit, temporally continuous link between source and target pixels. We propose Track2View, which conditions a video diffusion transformer on paired 3D point tracks: sparse trajectories of scene points projected into both the source and target camera views. These tracks provide explicit spatiotemporal correspondences that are temporally continuous by construction, encoding what content should appear where and when. At the core of Track2View is a dual-view track conditioner that transfers visual context from source to target view through parameter-free geometric operations and learned temporal aggregation, ensuring generalization to arbitrary camera trajectories without memorizing specific motions. We further introduce a data curation pipeline that extracts one-to-one track correspondences by running a 3D point tracker on temporally concatenated multi-camera view pairs. On a 400-video benchmark spanning static and dynamic scenes, Track2View achieves state-of-the-art results across visual quality, view synchronization, and camera accuracy, reducing rotation error by 30-65% and translation error by 61-72% relative to leading baselines. Project page is available at this https URL: https://qjizhi.github.io/track2view

翻译：从新摄像机视角重新渲染现有视频，要求输出遵循预设摄像机轨迹的同时，在每一帧中保留原始场景的外观与动态。现有方法依赖每帧姿态嵌入、含噪点云渲染或隐式学习对应关系，但均无法在源像素与目标像素之间提供显式且时间连续的关联。我们提出Track2View，该方法将视频扩散变换器与成对三维点轨迹条件相结合：这些轨迹是场景点投影到源和目标摄像机视角的稀疏路径。这些轨迹通过构造提供时间连续的显式时空对应关系，编码内容应在何时何处出现。Track2View的核心是双视角轨迹调节器，通过无参数几何运算和学习的时间聚合将视觉上下文从源视角传递至目标视角，从而在不记忆特定运动的前提下实现对任意摄像机轨迹的泛化。我们进一步引入数据整理流水线，通过在时间拼接的多视角摄像机对上运行三维点追踪器来提取一对一轨迹对应关系。在涵盖静态与动态场景的400段视频基准上，Track2View在视觉质量、视角同步性与摄像机精度方面均达到最优结果，相较于领先基线方法，旋转误差降低30-65%，平移误差降低61-72%。项目主页见此链接：https://qjizhi.github.io/track2view