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.

翻译：随着生成式AI的最新进展，自动操控三维模型正变得日益便捷。然而，现有方法通常对模型进行全局性编辑，这可能导致模型在物理世界制造时原有功能受损。例如，修改三维模型中的功能性结构（如花瓶底座）可能破坏其原始功能，导致花瓶倾倒。本文提出一种自动将三维模型分割为功能性与装饰性元素的方法，使用户能够选择性修改模型的美学结构而不影响功能部件。为开发该方法，我们首先通过对来自流行3D打印资源库Thingiverse的1000个模型进行定性分析，建立了三维模型功能分类法。基于该分类体系，我们开发了一种半自动分类方法，将三维模型分解为功能性与装饰性元素。我们提出名为Style2Fab的系统，使用户能够在保持功能完整的前提下对三维模型进行选择性风格化。通过与人工标注数据对比验证了分类方法的有效性，并通过用户研究展示了Style2Fab的实用价值——功能感知分割有助于保持模型功能完整性。