InterGSEdit: Interactive 3D Gaussian Splatting Editing with 3D Geometry-Consistent Attention Prior

TL;DR

InterGSEdit introduces an interactive framework for high-quality 3D Gaussian Splatting editing, utilizing user-selected views and a novel attention prior to enhance consistency and control in 3D scene editing.

Contribution

The paper proposes a new interactive editing framework with a semantic consistency selection strategy and a geometry-aware attention prior, improving multi-view consistency and user control in 3D Gaussian Splatting editing.

Findings

Achieves state-of-the-art 3D editing quality.

Enhances multi-view consistency and user control.

Demonstrates superior performance in experiments.

Abstract

3D Gaussian Splatting based 3D editing has demonstrated impressive performance in recent years. However, the multi-view editing often exhibits significant local inconsistency, especially in areas of non-rigid deformation, which lead to local artifacts, texture blurring, or semantic variations in edited 3D scenes. We also found that the existing editing methods, which rely entirely on text prompts make the editing process a "one-shot deal", making it difficult for users to control the editing degree flexibly. In response to these challenges, we present InterGSEdit, a novel framework for high-quality 3DGS editing via interactively selecting key views with users' preferences. We propose a CLIP-based Semantic Consistency Selection (CSCS) strategy to adaptively screen a group of semantically consistent reference views for each user-selected key view. Then, the cross-attention maps derived from the reference views are used in a weighted Gaussian Splatting unprojection to construct the 3D Geometry-Consistent Attention Prior ($GAP^{3D}$). We project $GAP^{3D}$ to obtain 3D-constrained attention, which are fused with 2D cross-attention via Attention Fusion Network (AFN). AFN employs an adaptive attention strategy that prioritizes 3D-constrained attention for geometric consistency during early inference, and gradually prioritizes 2D cross-attention maps in diffusion for fine-grained features during the later inference. Extensive experiments demonstrate that InterGSEdit achieves state-of-the-art performance, delivering consistent, high-fidelity 3DGS editing with improved user experience.