标题： GaVS：通过时间一致的局部重建和渲染进行3D定位的视频稳定化 显示英文标题

标题： GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering

摘要： 视频稳定对于视频处理至关重要，因为它可以去除不必要的抖动，同时保留原始的用户运动意图。 现有的方法，根据其操作的领域不同，存在一些问题（例如几何失真、过度裁剪、泛化能力差），这些问题会降低用户体验。 为了解决这些问题，我们引入了\textbf{GaVS}，一种新颖的3D基础方法，将视频稳定重新表述为一个时间一致的“局部重建和渲染”范式。 给定3D相机姿态，我们增强了一个重建模型以预测高斯点云基元，并在测试时进行微调，通过多视角动态感知的光度监督和跨帧正则化，以生成时间一致的局部重建。 然后使用该模型渲染每个稳定后的帧。 我们利用场景外推模块来避免帧裁剪。 我们的方法在一个重新使用的数据集上进行了评估，该数据集包含了带有3D基础信息的样本，涵盖了多种相机运动和场景动态的样本。 定量上，我们的方法在传统任务指标和新的几何一致性方面与最先进的2D和2.5D方法相比具有竞争力或更优。 定性上，我们的方法相比其他方法产生了明显更好的结果，这一点通过用户研究得到了验证。 显示英文摘要

摘要： Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent. Existing approaches, depending on the domain they operate, suffer from several issues (e.g. geometric distortions, excessive cropping, poor generalization) that degrade the user experience. To address these issues, we introduce \textbf{GaVS}, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent `local reconstruction and rendering' paradigm. Given 3D camera poses, we augment a reconstruction model to predict Gaussian Splatting primitives, and finetune it at test-time, with multi-view dynamics-aware photometric supervision and cross-frame regularization, to produce temporally-consistent local reconstructions. The model are then used to render each stabilized frame. We utilize a scene extrapolation module to avoid frame cropping. Our method is evaluated on a repurposed dataset, instilled with 3D-grounded information, covering samples with diverse camera motions and scene dynamics. Quantitatively, our method is competitive with or superior to state-of-the-art 2D and 2.5D approaches in terms of conventional task metrics and new geometry consistency. Qualitatively, our method produces noticeably better results compared to alternatives, validated by the user study.