ReplaceAnything3D：基于组合神经辐射场的文本引导3D场景编辑
ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
AI总结：
该研究提出文本引导的3D场景编辑模型ReplaceAnything3D（RAM3D），采用“擦除-替换”方法，可基于文本提示替换场景中特定物体，同时保持多视图3D一致性，在多种真实场景中实现了前景物体与场景的良好融合。
摘要：
我们提出了ReplaceAnything3D模型（RAM3D），这是一种新颖的文本引导3D场景编辑方法，可实现场景内特定物体的替换。给定场景的多视图图像、描述待替换物体的文本提示以及描述新物体的文本提示，我们的“擦除-替换”方法能有效将场景中的物体替换为新生成的内容，同时保持多视图间的3D一致性。我们通过将其应用于多种真实3D场景验证了ReplaceAnything3D的通用性，展示了修改后的前景物体与场景其余部分良好融合、不影响场景整体完整性的效果。
英文摘要：
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.
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。
