We propose MeshOn, a method that finds physically and semantically realistic compositions of two input meshes. Given an accessory, a base mesh with a user-defined target region, and optional text strings for both meshes, MeshOn uses a multi-step optimization framework to realistically fit the meshes onto each other while preventing intersections. We initialize the shapes' rigid configuration via a structured alignment scheme using Vision-to-Language Models, which we then optimize using a combination of attractive geometric losses, and a physics-inspired barrier loss that prevents surface intersections. We then obtain a final deformation of the object, assisted by a diffusion prior. Our method successfully fits accessories of various materials over a breadth of target regions, and is designed to fit directly into existing digital artist workflows. We demonstrate the robustness and accuracy of our pipeline by comparing it with generative approaches and traditional registration algorithms.

翻译：我们提出MeshOn，一种能够对两个输入网格实现物理与语义层面真实组合的方法。给定一个附件、一个带有用户定义目标区域的基础网格，以及两个网格的可选文本描述，MeshOn采用多阶段优化框架在防止相交的同时实现网格间的真实贴合。首先通过基于视觉-语言模型的结构化对齐方案初始化形状的刚性配置，然后结合几何吸引力损失与基于物理的屏障损失（用于防止表面相交）进行优化优化。最终借助扩散先验完成物体的形变。该方法能成功将各类材质的附件贴合到广泛的目标区域上，并专为适配现有数字艺术家工作流程而设计。通过与传统注册算法及生成式方法的对比，我们验证了该流水线的鲁棒性与精确性。