扩散模型是几何评判器：利用预训练扩散先验的单图像3D编辑
Diffusion Models are Geometry Critics: Single Image 3D Editing Using Pre-Trained Diffusion Priors
AI总结：
本研究提出一种基于预训练扩散模型的单图像3D编辑技术，通过迭代新视图合成与几何对齐算法，利用扩散模型的外观先验与几何评判功能，实现大视角变换下的高质量3D感知编辑，提升开放域泛化能力。
摘要：
我们提出了一种新颖的图像编辑技术，可对单张图像执行3D操作，例如物体旋转和平移。现有的3D感知图像编辑方法通常依赖合成多视图数据集来训练专用模型，因此限制了其在布局和风格差异更大的开放域图像上的效果。相比之下，我们的方法直接利用在广泛文本-图像对上训练的强大图像扩散模型，从而保留其出色的泛化能力。这一目标通过开发一种迭代新视图合成与几何对齐算法来实现。该算法将扩散模型用于双重目的：它们通过利用估计的深度图预测所选物体的新视图来提供外观先验，同时通过校正采样视图中3D形状的错位来充当几何评判器。我们的方法能够生成高质量的3D感知图像编辑结果，支持大视角变换，且与输入图像在外观和形状上具有高度一致性，拓展了单图像3D感知编辑的可行边界。
英文摘要：
We propose a novel image editing technique that enables 3D manipulations on single images, such as object rotation and translation. Existing 3D-aware image editing approaches typically rely on synthetic multi-view datasets for training specialized models, thus constraining their effectiveness on open-domain images featuring significantly more varied layouts and styles. In contrast, our method directly leverages powerful image diffusion models trained on a broad spectrum of text-image pairs and thus retain their exceptional generalization abilities. This objective is realized through the development of an iterative novel view synthesis and geometry alignment algorithm. The algorithm harnesses diffusion models for dual purposes: they provide appearance prior by predicting novel views of the selected object using estimated depth maps, and they act as a geometry critic by correcting misalignments in 3D shapes across the sampled views. Our method can generate high-quality 3D-aware image edits with large viewpoint transformations and high appearance and shape consistency with the input image, pushing the boundaries of what is possible with single-image 3D-aware editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
