Localized Gaussian Splatting Editing with Contextual Awareness

Hanyuan Xiao, Yingshu Chen, Huajian Huang, Haolin Xiong, Jing Yang, Pratusha Prasad, Yajie Zhao; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5207-5217

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 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.