UniGeo：馴服視頻擴散以實現統一且一致的幾何估計

摘要

近期，利用扩散模型先验辅助单目几何估计（如深度和法线）的方法因其强大的泛化能力而受到广泛关注。然而，现有研究大多集中于在单个视频帧的相机坐标系内估计几何属性，忽视了扩散模型在确定帧间对应关系方面的固有能力。在本研究中，我们通过适当的设计和微调，证明了视频生成模型的内在一致性可被有效利用于一致的几何估计。具体而言，我们1）选择在全局坐标系中与视频帧具有相同对应关系的几何属性作为预测目标，2）通过重用位置编码引入了一种新颖且高效的条件化方法，以及3）通过对共享相同对应关系的多个几何属性进行联合训练来提升性能。我们的结果在预测视频中的全局几何属性方面表现出色，并可直接应用于重建任务。即使仅在静态视频数据上进行训练，我们的方法也展现出泛化到动态视频场景的潜力。

English

Recently, methods leveraging diffusion model priors to assist monocular geometric estimation (e.g., depth and normal) have gained significant attention due to their strong generalization ability. However, most existing works focus on estimating geometric properties within the camera coordinate system of individual video frames, neglecting the inherent ability of diffusion models to determine inter-frame correspondence. In this work, we demonstrate that, through appropriate design and fine-tuning, the intrinsic consistency of video generation models can be effectively harnessed for consistent geometric estimation. Specifically, we 1) select geometric attributes in the global coordinate system that share the same correspondence with video frames as the prediction targets, 2) introduce a novel and efficient conditioning method by reusing positional encodings, and 3) enhance performance through joint training on multiple geometric attributes that share the same correspondence. Our results achieve superior performance in predicting global geometric attributes in videos and can be directly applied to reconstruction tasks. Even when trained solely on static video data, our approach exhibits the potential to generalize to dynamic video scenes.

UniGeo：馴服視頻擴散以實現統一且一致的幾何估計

UniGeo: Taming Video Diffusion for Unified Consistent Geometry Estimation

摘要

Support