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

翻译：近年来，众多技术已涌现用于创建高质量的三维资产与场景。然而，在对这些对象进行编辑时，现有方法要么速度缓慢，要么在质量上有所妥协，或未能提供足够的定制化能力。本文提出一种新颖方法，能够基于单张参考视图快速编辑三维模型。我们的技术首先对编辑图像进行分割，随后利用DINO特征在选定的分割数据集视图间匹配语义对应的区域。编辑图像特定区域的颜色或纹理变化随后能以语义合理的方式自动应用于其他视图。这些经过编辑的视图作为更新的数据集，用于进一步训练并重新风格化三维场景。最终结果即为编辑后的三维模型。我们的框架支持多种编辑任务，例如手动局部编辑、基于对应关系的任意示例图像风格迁移，以及多张示例图像不同风格的组合。我们采用高斯点云作为主要的三维表示方法，因其速度快且易于局部编辑，但本技术同样适用于其他方法（如NeRF）。通过多个示例，我们展示了本方法在提供细粒度编辑控制的同时，能够产生更高质量的结果。项目页面：ice-gaussian.github.io