  The advancement of text-driven 3D content editing has been blessed by the
progress from 2D generative diffusion models. However, a major obstacle
hindering the widespread adoption of 3D content editing is its time-intensive
processing. This challenge arises from the iterative and refining steps
required to achieve consistent 3D outputs from 2D image-based generative
models. Recent state-of-the-art methods typically require optimization time
ranging from tens of minutes to several hours to edit a 3D scene using a single
GPU. In this work, we propose that by incorporating correspondence
regularization into diffusion models, the process of 3D editing can be
significantly accelerated. This approach is inspired by the notion that the
estimated samples during diffusion should be multiview-consistent during the
diffusion generation process. By leveraging this multiview consistency, we can
edit 3D content at a much faster speed. In most scenarios, our proposed
technique brings a 10$\times$ speed-up compared to the baseline method and
completes the editing of a 3D scene in 2 minutes with comparable quality.
