ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
This enables text-guided 3D scene editing for applications like virtual reality or content creation, but it appears incremental as it builds on existing neural radiance field methods.
The paper tackles the problem of editing 3D scenes by replacing specific objects based on text prompts, achieving results that maintain 3D consistency and integrate well with the scene.
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.