GSEdit: Efficient Text-Guided Editing of 3D Objects via Gaussian
  Splatting
Francesco Palandra Andrea Sanchietti Daniele Baieri Emanuele Rodolà
We present GSEdit, a pipeline for text-guided 3D object editing based on
Gaussian Splatting models. Our method enables the editing of the style and
appearance of 3D objects without altering their main details, all in a matter
of minutes on consumer hardware. We tackle the problem by leveraging Gaussian
splatting to represent 3D scenes, and we optimize the model while progressively
varying the image supervision by means of a pretrained image-based diffusion
model. The input object may be given as a 3D triangular mesh, or directly
provided as Gaussians from a generative model such as DreamGaussian. GSEdit
ensures consistency across different viewpoints, maintaining the integrity of
the original object's information. Compared to previously proposed methods
relying on NeRF-like MLP models, GSEdit stands out for its efficiency, making
3D editing tasks much faster. Our editing process is refined via the
application of the SDS loss, ensuring that our edits are both precise and
accurate. Our comprehensive evaluation demonstrates that GSEdit effectively
alters object shape and appearance following the given textual instructions
while preserving their coherence and detail.