DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
It addresses the problem of controlled 3D scene editing for users in computer graphics and vision, representing an incremental advancement over existing neural field techniques.
The paper tackles the challenge of editing neural fields for 3D scenes by introducing DreamEditor, a framework that uses text prompts to enable localized editing, resulting in highly realistic textures and geometry that surpass previous methods in evaluations.
Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations.