ReplaceAnything3D：使用组合神经辐射场进行文本引导的3D场景编辑

ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields

January 31, 2024

作者: Edward Bartrum, Thu Nguyen-Phuoc, Chris Xie, Zhengqin Li, Numair Khan, Armen Avetisyan, Douglas Lanman, Lei Xiao

cs.AI

摘要

我们介绍了一种名为ReplaceAnything3D模型（RAM3D）的新型文本引导的3D场景编辑方法，该方法可以替换场景中的特定对象。给定场景的多视角图像、描述要替换的对象的文本提示以及描述新对象的文本提示，我们的擦除和替换方法可以有效地在场景中交换对象，并使用新生成的内容保持多个视角的3D一致性。我们展示了ReplaceAnything3D的多功能性，将其应用于各种逼真的3D场景，展示了修改后的前景对象的结果，这些对象与场景的其余部分完美融合，而不影响整体完整性。

English

We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images of a scene, a text prompt describing the object to replace, and a text prompt describing the new object, our Erase-and-Replace approach can effectively swap objects in the scene with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of ReplaceAnything3D by applying it to various realistic 3D scenes, showcasing results of modified foreground objects that are well-integrated with the rest of the scene without affecting its overall integrity.