[论文] InstructMesh: Selective Refinement of Generative 3D Models for Fabrica...

论文概要

研究领域: ML 作者: Faraz Faruqi, Ahmed Katary, Demircan Tas, Theresa Hradilak, Ning Zhang, Jiaji Li, Fabian Manhardt, Martin Nisser, Vrushank Phadnis, Ruofei Du, Federico Tombari, Megan Hofmann, Stefanie Mueller 发布时间: 2026-08-28 arXiv: 2608.28534

中文摘要

生成式AI的最新进展允许用户从文本或图像创建3D模型。然而，这些模型优先考虑视觉合理性而非几何精度，通常生成具有缺陷的结果，这些缺陷会损害其制造后的预期用途。我们推出了InstructMesh，一个交互式生成后精修工具，通过区域选择和有针对性的操作（如打开或密封空隙，或调整局部厚度）实现对生成3D模型的选择性修复。用户可以通过自然语言提示或滑块控件调用编辑操作。通过在中间潜在表示上直接操作，InstructMesh允许用户应用稳健的几何校正，无需专家建模技能。为告知我们的设计，我们首先分析了最先进的生成工具输出中常见的制造相关失效模式。然后我们进行了两项用户研究，证明新手可以使用InstructMesh对生成输出执行制造相关修复，并揭示用户对结合滑块控件和自然语言输入的混合界面的偏好。

原文摘要

Recent advances in generative AI allow users to create 3D models from text or images. However, these models prioritize visual plausibility over geometric accuracy, often generating results with flaws that compromise their intended use post-fabrication. We present InstructMesh, an interactive post-generation refinement tool that enables selective repair of generative 3D models through region selection and targeted operations, such as opening or sealing voids, or adjusting local thickness. Users can invoke edit operations via natural language prompts or slider controls. By operating directly on the intermediate latent representation, InstructMesh allows users to apply robust geometric corrections without requiring expert modeling skills. To inform our design, we first analyze common fabricat...

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