发表机构
The Hong Kong University of Science and Technology; The Chinese University of Hong Kong（香港科学与技术大学; 香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D软件中碰撞网格编辑难题，提出神经符号程序合成方法，将任务转化为例程编程问题，通过MeshForge工具实现，在6 hundred个碰撞网格的24个任务中评估，成功合成多数任务，平均所需演示次数和合成时间较少。
AI 中文摘要
随着3D软件的激增，软件工件现在不仅包括代码和2D用户界面，还包括3D资产。其中，碰撞网格至关重要，因为它们定义了物理引擎用于碰撞检测和物理交互的几何形状。现有工具虽能自动生成碰撞网格，但常无法捕捉预期交互行为，开发者需手动编辑许多异构碰撞网格，既耗时又难扩展。为解决此问题，我们提出一种神经符号程序合成方法用于批量编辑碰撞网格。将任务表述为例程编程问题：给定具有相同编辑意图的一系列碰撞网格和少量用户演示，该方法合成可复用程序以捕捉编辑意图并应用于非演示网格。我们在名为MeshForge的工具中实现此方法，并在600个碰撞网格上的24个任务中进行评估。MeshForge成功合成了23/24个任务，平均需要2.2次演示和3.5秒的合成时间。
英文摘要
As 3D software proliferates, software artifacts now extend beyond code and 2D user interfaces to include 3D assets. Among these assets, collision meshes are critical as they define the geometry used by physics engines for collision detection and physical interaction. Although existing tools can automatically generate collision meshes from visual meshes, they often fail to capture the intended interaction behavior. As a result, developers need to manually edit many heterogeneous collision meshes, a process that is time-consuming and challenging to scale. To address this problem, we present a neuro-symbolic program synthesis approach for batch-editing collision meshes. We formulate the task as a programming-by-example problem: given a family of collision meshes with the same editing intent and a small number of user demonstrations, our approach synthesizes a reusable program that captures the editing intent and applies it to non-demonstration meshes. We implement this in a tool named MeshForge, and evaluate it across 24 tasks on 600 collision meshes. MeshForge successfully synthesizes 23/24 tasks, requiring 2.2 demonstrations and 3.5 seconds of synthesis time on average.
