The ability to edit 3D assets from natural language presents a compelling paradigm to aid in the democratization of 3D content creation. However, while natural language is often effective at communicating general intent, it is poorly suited for specifying precise manipulation. To address this gap, we introduce ParSEL, a system that enables controllable editing of high-quality 3D assets from natural language. Given a segmented 3D mesh and an editing request, ParSEL produces a parameterized editing program. Adjusting the program parameters allows users to explore shape variations with a precise control over the magnitudes of edits. To infer editing programs which align with an input edit request, we leverage the abilities of large-language models (LLMs). However, while we find that LLMs excel at identifying initial edit operations, they often fail to infer complete editing programs, and produce outputs that violate shape semantics. To overcome this issue, we introduce Analytical Edit Propagation (AEP), an algorithm which extends a seed edit with additional operations until a complete editing program has been formed. Unlike prior methods, AEP searches for analytical editing operations compatible with a range of possible user edits through the integration of computer algebra systems for geometric analysis. Experimentally we demonstrate ParSEL's effectiveness in enabling controllable editing of 3D objects through natural language requests over alternative system designs.

翻译：通过自然语言编辑三维资产的能力为促进三维内容创作的民主化提供了一种引人注目的范式。然而，尽管自然语言在传达总体意图方面通常有效，却不适用于指定精确的操作。为弥补这一差距，我们提出了ParSEL系统，该系统能够基于自然语言对高质量三维资产进行可控编辑。给定一个已分割的三维网格和一个编辑请求，ParSEL会生成一个参数化的编辑程序。调整程序参数可使用户探索形状的多种变体，并能精确控制编辑的幅度。为了推断与输入编辑请求相符的编辑程序，我们利用了大型语言模型（LLMs）的能力。然而，我们发现虽然LLMs擅长识别初始编辑操作，却常常无法推断出完整的编辑程序，并且其输出可能违反形状语义。为克服此问题，我们提出了解析编辑传播（AEP）算法，该算法通过添加额外操作来扩展种子编辑，直至形成完整的编辑程序。与先前方法不同，AEP通过集成用于几何分析的计算机代数系统，搜索与一系列可能的用户编辑相兼容的解析编辑操作。实验证明，相较于其他系统设计方案，ParSEL能够通过自然语言请求实现对三维对象的可控编辑，验证了其有效性。