SIn-NeRF2NeRF：通过分割和修复基于指令编辑3D场景
SIn-NeRF2NeRF: Editing 3D Scenes with Instructions through Segmentation and Inpainting
AI总结：
本文提出SIn-NeRF2NeRF方法，通过将3D对象与背景场景分离，分别进行对象分割和背景修复，实现对3D场景中对象几何变化的选择性编辑。
摘要：
TL;DR 通过将3D对象与背景场景分离，实现选择性3D对象编辑。Instruct-NeRF2NeRF (in2n) 是一种很有前景的方法，能够使用文本提示编辑由神经辐射场 (NeRF) 组成的3D场景。然而，同时对背景和对象执行几何修改（如收缩、缩放或移动）具有挑战性。在本项目中，我们通过将对象从场景中分离后进行选择性编辑，来实现3D场景内对象的几何变化。我们分别执行对象分割和背景修复，并展示了在三维空间中自由调整大小或移动分离对象的各种示例。
英文摘要：
TL;DR Perform 3D object editing selectively by disentangling it from the background scene. Instruct-NeRF2NeRF (in2n) is a promising method that enables editing of 3D scenes composed of Neural Radiance Field (NeRF) using text prompts. However, it is challenging to perform geometrical modifications such as shrinking, scaling, or moving on both the background and object simultaneously. In this project, we enable geometrical changes of objects within the 3D scene by selectively editing the object after separating it from the scene. We perform object segmentation and background inpainting respectively, and demonstrate various examples of freely resizing or moving disentangled objects within the three-dimensional space.
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。
