Localized NeRF Editing With Prompt-Guided 3D Volume Blending
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for editing or modifying 3D objects represented by neural radiance fields (NeRFs) are inadequate for targeted and localized modifications, as they often require regenerating the entire object or lack precision in adding details, leading to inefficiencies and increased editing time.
Innovation Solution
A system and method for localized editing of NeRFs by allowing users to select specific 3D volumes and provide prompts for modifications, using diffusion models to generate new content, and blending the original and modified NeRFs to create a combined representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diffusion models or stylization models are used to generate or modify 3D objects, then novel views or modified properties can be achieved, but targeted editing or localized modifications become difficult
Solution Approach 1:
The patent divides the 3D object into multiple editable regions or segments, allowing users to select and modify specific portions independently. This segmentation enables targeted editing by separating the object into manageable parts that can be individually processed by diffusion models, resolving the contradiction between generating novel views and enabling localized modifications.
Solution Approach 2:
The patent applies different modification qualities or operations to different regions of the 3D object. By allowing local customization of editing parameters, diffusion models can be applied with region-specific control, enabling precise localized modifications while maintaining the ability to generate diverse novel views across different areas of the object.
2Manufacturing precision
If entire 3D objects are regenerated to make modifications, then comprehensive changes can be achieved, but editing time increases
Solution Approach 1:
The patent extracts or isolates the specific regions requiring modification from the complete 3D object. By separating only the necessary portions for editing and applying diffusion models selectively to these extracted regions, the system achieves comprehensive modifications to the target areas without the time cost of regenerating the entire object, thus resolving the contradiction between modification completeness and editing efficiency.
Solution Approach 2:
The patent applies the principle of partial action by performing modifications only on the specific regions that require changes rather than the entire object. This selective approach uses diffusion models partially on subsets of the 3D object, achieving sufficient modification completeness for the targeted areas while significantly reducing the overall editing time compared to full object regeneration.
3Adaptability or versatility
If stylization models are used to stretch or modify existing 3D objects, then property modifications can be achieved, but precision in adding details is limited
Solution Approach 1:
The patent introduces an intermediary processing step between the original 3D object and the final modified result. Diffusion models serve as intermediaries that first generate detailed modifications in a controlled manner, which are then integrated back into the original object. This intermediary approach enables precise detail addition by allowing the diffusion model to focus on generating high-fidelity local content before combining it with the parent object, resolving the contradiction between property modification versatility and detail precision.
Data Source
AI summary
Systems and methods of the present disclosure include interactive editing for generated three-dimensional (3D) models, such as those represented by neural radiance fields (NeRFs). A 3D model may be presented to a user in which the user may identify one or more localized regions for editing and/or modification. The localized regions may be selected and a corresponding 3D volume for that region may be provided to one or more generative networks, along with a prompt, to generate new content for the localized regions. Each of the original NeRF and the newly generated NeRF for the new content may then be combined into a single NeRF for a combined 3D representation with the original content and the localized modifications.