With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.

通过对1000个来自流行的3D打印库Thingiverse的模型进行定性分析，我们首先创建了一个功能的分类法，再开发了一种半自动分类方法来将3D模型分解为功能和美学元素，并提出了一个名为Style2Fab的系统，该系统允许用户无损修改3D模型的外观。通过与人工注释数据的比较评估我们的分类方法的有效性，并通过用户研究展示功能感知的分割有助于保持模型的功能。

Style2Fab: 面向功能的分割用于使用生成 AI 制作个性化 3D 模型