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.
近年来，许多技术涌现出来用于创建高质量的3D资产和场景。然而，在编辑这些对象时，现有的方法要么速度慢，要么在质量上妥协，要么无法提供足够的定制化。我们提出了一种从单一参考视图快速编辑3D模型的新方法。我们的技术首先对编辑图像进行分割，然后使用DINO特征匹配选择的分割数据集视图中语义上对应的区域。然后可以以语义上合理的方式自动将编辑图像的特定区域的颜色或纹理变化应用到其他视图。这些编辑后的视图充当更新的数据集，用于进一步训练和重新风格化3D场景。因此最终结果是一个被编辑的3D模型。我们的框架支持各种编辑任务，如手动局部编辑、基于对应关系的样式转移，以及结合多个示例图像的不同风格。我们使用高斯喷溅作为主要的3D表示，因为它们在速度和局部编辑方面的便利性，但我们的技术也适用于其他方法，如NeRFs。我们通过多个示例展示，我们的方法在提供精细控制编辑方面产生了更高质量的结果。