# Track2View：通过配对3D点轨迹实现4D一致的相机可控视频生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-14 08:00
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmqguyr0h00t5slpuzmg5gobj
- 原文链接：https://arxiv.org/abs/2606.15534

## AI 摘要

Track2View将视频扩散Transformer与配对的3D点轨迹条件结合，通过源视图和目标视图中场景点的稀疏轨迹提供显式、时序连续的时空对应。其双视角轨迹调节器利用无参数几何操作和时序聚合转移视觉上下文，能泛化到任意相机轨迹。在含400个视频（静态和动态场景）的基准测试中，Track2View在视觉质量、视图同步和相机精度上均达最优，旋转误差比领先基线降低30-65%，平移误差降低61-72%。

## 正文

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