三维空间中的二维实例编辑
2D Instance Editing in 3D Space
AI总结：
针对二维图像编辑中一致性与身份保持难题，提出“二维-三维-二维”框架，将对象提升至三维刚性环境编辑后重投影回二维，超越DragGAN等方法，实现高一致性与身份保持。
摘要：
生成模型在推进二维图像编辑方面取得了显著进展，展现出卓越的精度和逼真度。然而，由于其固有的像素操作特性，它们往往在一致性和对象身份保持方面存在困难。为解决这一局限，我们引入了一种新颖的“二维-三维-二维”框架。我们的方法首先将二维对象提升为三维表示，从而能够在物理上合理、受刚性约束的三维环境中进行编辑。编辑后的三维对象随后被重新投影并无缝修复回原始二维图像。与现有的二维编辑方法（如DragGAN和DragDiffusion）相比，我们的方法直接在三维环境中操作对象。大量实验表明，我们的框架在整体性能上超越了先前的方法，在提供高度一致编辑的同时，稳健地保持了对象身份。
英文摘要：
Generative models have achieved significant progress in advancing 2D image editing, demonstrating exceptional precision and realism. However, they often struggle with consistency and object identity preservation due to their inherent pixel-manipulation nature. To address this limitation, we introduce a novel "2D-3D-2D" framework. Our approach begins by lifting 2D objects into 3D representation, enabling edits within a physically plausible, rigidity-constrained 3D environment. The edited 3D objects are then reprojected and seamlessly inpainted back into the original 2D image. In contrast to existing 2D editing methods, such as DragGAN and DragDiffusion, our method directly manipulates objects in a 3D environment. Extensive experiments highlight that our framework surpasses previous methods in general performance, delivering highly consistent edits while robustly preserving object identity.
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。
