CUESplat: Guiding Gaussian Scene Updates with Paired 3D Edit Inference

Existing text-driven Gaussian editing methods commonly edit rendered views and fit the source Gaussians to the resulting images. Due to geometric changes and multi-view inconsistencies generated during the editing process, image fitting alone is not enough to specify which source surfaces to retain or reconstruct. We present CUESplat, which uses paired 3D edit inference to guide these scene updates. During denoising, a geometry predictor updates edited depths in the source scene frame and returns them to subsequent steps for cross-view coupling. After candidate generation, paired source and edited geometry provides displacement evidence, checked against source geometry and across views, to guide local Gaussian updates. To preserve usable source content, CUESplat reuses existing surfaces before introducing geometry for missing content. This couples structural revision with image fitting for appearance modification, object addition, and object removal. Experiments demonstrate that our method outperforms existing state-of-the-art Gaussian editing methods on three tasks.