DATENeRF: Depth-Aware Text-based Editing of NeRFs
This addresses the challenge of applying text-based editing to 3D scenes for researchers and practitioners in computer vision and graphics, though it is incremental as it builds on existing diffusion and NeRF techniques.
The paper tackles the problem of extending text-based image editing to Neural Radiance Fields (NeRF) scenes by using the scene's geometry as a bridge to integrate 2D edits, resulting in more consistent, lifelike, and detailed edits than existing leading methods.
Recent advancements in diffusion models have shown remarkable proficiency in editing 2D images based on text prompts. However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views. Our crucial insight is that a NeRF scene's geometry can serve as a bridge to integrate these 2D edits. Utilizing this geometry, we employ a depth-conditioned ControlNet to enhance the coherence of each 2D image modification. Moreover, we introduce an inpainting approach that leverages the depth information of NeRF scenes to distribute 2D edits across different images, ensuring robustness against errors and resampling challenges. Our results reveal that this methodology achieves more consistent, lifelike, and detailed edits than existing leading methods for text-driven NeRF scene editing.