Year: 2025
Type: conference-paper
Abstract: Recent advancements in text-guided 3D object generation using diffusion priors struggle with illumination inconsistencies when applied to scene editing tasks like object replacement or insertion. To address this, we propose an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS). Our method leverages state-of-the-art 2D diffusion inpainting [56] to handle global illumination context effectively. Specifically, we identify representative anchor views that capture scene-wide illumination, inpaint them using 2D diffusion models, and integrate the results into a coarse-to-fine 3DGS optimization process. In the fine step, we introduce Depth-guided Inpainting Score Distillation Sampling (DI-SDS) to refine geometry and texture details, capitalizing on the diversity of 2D priors. Our approach achieves locally precise edits with globally consistent illumination, demonstrating robustness in real scenes with highlights and shadows. Comparisons show superior results over state-of-the-art text-to-3D editing methods. Project page: https://corneliushsiao.github.io/GSLE.html.
Authors: Hanyuan Xiao, Yingshu Chen, Huajian Huang, Haolin Xiong, Jing Yang, Pratusha Prasad, Yajie Zhao
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Army, Office of Naval Research
Awards: G2651332063, G7807198690
OpenAlex ID: W4409262146