The 3D computer vision domain was flooded with NeRFs in recent times. They emerged as a groundbreaking technique and enabled the reconstruction and synthesis of novel views of a scene. NeRFs capture and model the underlying geometry and appearance information from a group of multi-view images.

By leveraging neural networks, NeRFs offer a data-driven approach that surpasses traditional methods. The neural networks in NeRFs learn to represent the complex relationship between scene geometry, lighting, and view-dependent appearance, allowing for highly detailed and realistic scene reconstructions. The important thing advantage of NeRFs lies of their ability to generate photo-realistic images from any desired viewpoint inside a scene, even in regions that weren’t captured by the unique set of images.

The success of NeRFs has opened up latest possibilities in computer graphics, virtual reality, and augmented reality, enabling the creation of immersive and interactive virtual environments that closely resemble real-world scenes. Due to this fact, there may be a serious interest within the domain to advance NeRFs even further.

Some drawbacks of NeRFs limit their applicability in real-world scenarios. For instance, editing neural fields is a big challenge attributable to the implicit encoding of the form and texture information inside high-dimensional neural network features. While some methods tried to tackle this using explored editing techniques, they often require extensive user input and struggle to attain precise and high-quality results. 

The flexibility to edit NeRFs can open possibilities in real-world applications. Nevertheless, to this point, all of the attempts weren’t adequate for them to unravel the issues. Well, we now have a brand new player in the sport, and it’s named DreamEditor.

DreamEditor is a user-friendly framework that enables intuitive and convenient modification of neural fields using text prompts. By representing the scene with a mesh-based neural field and employing a stepwise editing framework, DreamEditor enables a big selection of editing effects, including re-texturing, object substitute, and object insertion.

The mesh representation facilitates precise local editing by converting 2D editing masks into 3D editing regions while also disentangling geometry and texture to forestall excessive deformation. The stepwise framework combines pre-trained diffusion models with rating distillation sampling, allowing efficient and accurate editing based on easy text prompts. 

DreamEditor follows three key stages to facilitate intuitive and precise text-guided 3D scene editing. Within the initial stage, the unique neural radiance field is transformed right into a mesh-based neural field. This mesh representation enables spatially-selective editing. After the conversion, it employs a customized Text-to-Image (T2I) model that’s trained on the precise scene to capture the semantic relationships between keywords within the text prompts and the scene’s visual content. Finally, the edited modifications are applied to the goal object throughout the neural field using the T2I diffusion mode.

DreamEditor can accurately and progressively edit the 3D scene while maintaining a high level of fidelity and realism. This stepwise approach, from mesh-based representation to express localization and controlled editing through diffusion models, allows DreamEditor to attain highly realistic editing results while minimizing unnecessary modifications in irrelevant regions.

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