  Recently many techniques have emerged to create high quality 3D assets and
scenes. When it comes to editing of these objects, however, existing approaches
are either slow, compromise on quality, or do not provide enough customization.
We introduce a novel approach to quickly edit a 3D model from a single
reference view. Our technique first segments the edit image, and then matches
semantically corresponding regions across chosen segmented dataset views using
DINO features. A color or texture change from a particular region of the edit
image can then be applied to other views automatically in a semantically
sensible manner. These edited views act as an updated dataset to further train
and re-style the 3D scene. The end-result is therefore an edited 3D model. Our
framework enables a wide variety of editing tasks such as manual local edits,
correspondence based style transfer from any example image, and a combination
of different styles from multiple example images. We use Gaussian Splats as our
primary 3D representation due to their speed and ease of local editing, but our
technique works for other methods such as NeRFs as well. We show through
multiple examples that our method produces higher quality results while
offering fine-grained control of editing. Project page: ice-gaussian.github.io
