DreamEditor：基于文本的神经场3D场景编辑

DreamEditor: Text-Driven 3D Scene Editing with Neural Fields

June 23, 2023

作者: Jingyu Zhuang, Chen Wang, Lingjie Liu, Liang Lin, Guanbin Li

cs.AI

摘要

神经场在视图合成和场景重建方面取得了令人瞩目的进展。然而，由于几何和纹理信息的隐式编码，编辑这些神经场仍然具有挑战性。在本文中，我们提出了DreamEditor，这是一个新颖的框架，使用户能够使用文本提示对神经场进行受控编辑。通过将场景表示为基于网格的神经场，DreamEditor允许在特定区域内进行局部编辑。DreamEditor利用预训练的文本到图像扩散模型的文本编码器，根据文本提示的语义自动识别要编辑的区域。随后，DreamEditor通过得分蒸馏采样优化编辑区域，并将其几何和纹理与文本提示对齐。大量实验证明，DreamEditor能够根据给定的文本提示准确编辑真实场景的神经场，同时确保无关区域的一致性。DreamEditor生成高度逼真的纹理和几何，明显超越了以往在定量和定性评估中的作品。

English

Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations.