用少量示范自动批量修复3D碰撞网格，提升物理交互准确性
Programming-by-Example for Batch-Editing Collision Meshes in 3D Software
- 通过编程示例学习用户意图，自动生成可复用的编辑程序
- 在600个网格上完成24项任务，成功率96%，平均仅需2.2次示范
- 适合需要批量处理3D物理碰撞网格的开发者与游戏引擎工程师
随着3D软件日益普及，软件资产已从代码和2D界面扩展至3D资源。其中，碰撞网格至关重要，它定义了物理引擎用于碰撞检测与物理交互的几何形状。尽管现有工具能从视觉网格自动生成碰撞网格，但常无法准确捕捉预期的交互行为。因此，开发者需手动编辑大量异构的碰撞网格，这一过程耗时且难以扩展。为此，我们提出一种神经符号式程序合成方法，用于批量编辑碰撞网格。将任务建模为编程示例问题：给定一组具有相同编辑意图的碰撞网格及少量用户示范，该方法合成一个可复用的程序，捕获编辑意图并应用于非示范网格。我们在名为MeshForge的工具中实现该方法，并在600个碰撞网格上的24个任务中进行评估。结果表明，MeshForge成功合成23/24个任务，平均仅需2.2次示范和3.5秒合成时间。
原文摘要 · Abstract (English)
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.
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