  Text-to-Image (T2I) diffusion models have recently gained traction for their
versatility and user-friendliness in 2D content generation and editing.
However, training a diffusion model specifically for 3D scene editing is
challenging due to the scarcity of large-scale datasets. Currently, editing 3D
scenes necessitates either retraining the model to accommodate various 3D edits
or developing specific methods tailored to each unique editing type. Moreover,
state-of-the-art (SOTA) techniques require multiple synchronized edited images
from the same scene to enable effective scene editing. Given the current
limitations of T2I models, achieving consistent editing effects across multiple
images remains difficult, leading to multi-view inconsistency in editing. This
inconsistency undermines the performance of 3D scene editing when these images
are utilized. In this study, we introduce a novel, training-free 3D scene
editing technique called \textsc{Free-Editor}, which enables users to edit 3D
scenes without the need for model retraining during the testing phase. Our
method effectively addresses the issue of multi-view style inconsistency found
in state-of-the-art (SOTA) methods through the implementation of a single-view
editing scheme. Specifically, we demonstrate that editing a particular 3D scene
can be achieved by modifying only a single view. To facilitate this, we present
an Edit Transformer that ensures intra-view consistency and inter-view style
transfer using self-view and cross-view attention mechanisms, respectively. By
eliminating the need for model retraining and multi-view editing, our approach
significantly reduces editing time and memory resource requirements, achieving
runtimes approximately 20 times faster than SOTA methods. We have performed
extensive experiments on various benchmark datasets, showcasing the diverse
editing capabilities of our proposed technique.
