D2Gaussian: Dynamic Control with Discretized 3D View Modeling for Text-Driven 3D Gaussian Splatting Editing

Current advances in text-driven 3D scene editing tasks typically render the 3D representations into multi-view images and modify the images with the text instructions. Context consistency across multiple views and cross-modal consistency in the single-view are the keys to effective 3D editing. Accordingly, existing methods introduce additional image constraints and apply pre-trained 2D editing models. However, they fix the same text instruction across all views and freeze the pre-trained 2D model for single-view editing, leading to deficient modeling of 3D scene views and results in inconsistent generations with visual artifacts. To address these limitations, we introduce a discretized 3D view modeling method and a diffusion-based multi-view consistent editing pipeline for text-driven 3D gaussian splatting editing, abbreviated as D2Gaussian. Specifically, our approach constructs a codebook that encodes continuous 3D view information into discrete token embeddings to model the spatial feature expressions. Then, the token embeddings are proposed to guide and finetune the diffusion-based image editing model with the dynamic addition of control conditions, yielding a multi-view consistent editing pipeline. Finally, we introduce a 3D editing dataset generation approach along with a 3D-CLIP-SIM metric to form a benchmark, 3D-MagicBrush, to provide more diverse evaluation scenarios for future 3D editing works. Experiments demonstrate that our method achieves better visual results and multi-view consistency than previous state-of-the-art methods.