LaVR：基于大型4D重建模型场景潜变量条件的生成式视频轨迹重渲染
LaVR: Scene Latent Conditioned Generative Video Trajectory Re-Rendering using Large 4D Reconstruction Models
用大型4D重建模型的潜空间作为隐式几何条件来驱动视频扩散生成，绕开显式深度估计与重建，为需要几何一致性的视频生成/新视角合成提供了一条可复用的条件注入路径。
原文依据 using the implicit geometric knowledge embedded in the latent space of a large 4D reconstruction model to condition the video generation process
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论给定一段单目视频，视频重渲染的目标是生成沿新相机轨迹观察该场景的视图。现有方法面临两个不同挑战。几何上无条件约束的模型缺乏空间感知能力，在视角变化下会出现漂移与形变。另一方面，几何条件模型依赖估计深度与显式重建，因而易受深度不准确与标定误差的影响。我们提出利用大型4D重建模型潜空间中嵌入的隐式几何知识来对视频生成过程施加条件，以应对上述挑战。这些潜变量在连续空间中刻画场景结构，无需显式重建。因此，它们提供了一种灵活的表征，使预训练扩散先验能够更有效地对误差进行正则化。通过联合以这些潜变量与源相机位姿为条件，我们证明该模型在视频重渲染任务上取得了最优结果。
英文原文摘要 1036 字符
Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to drift and deformation under viewpoint changes. On the other hand, geometrically-conditioned models depend on estimated depth and explicit reconstruction, making them susceptible to depth inaccuracies and calibration errors.We propose to address these challenges by using the implicit geometric knowledge embedded in the latent space of a large 4D reconstruction model to condition the video generation process. These latents capture scene structure in a continuous space without explicit reconstruction. Therefore, they provide a flexible representation that allows the pretrained diffusion prior to regularize errors more effectively. By jointly conditioning on these latents and source camera poses, we demonstrate that our model achieves state-of-the-art results on the video re-rendering task.