InstructMesh：面向制造场景的生成式3D模型选择性优化
InstructMesh: Selective Refinement of Generative 3D Models for Fabrication
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中文总结 AI 辅助
InstructMesh是一款交互式3D模型后生成优化工具，可通过区域选择、针对性操作及自然语言或滑块控件，帮助用户修复生成式3D模型的制造相关缺陷，新手无需专业技能即可使用，且用户偏好混合交互界面。
中文摘要 AI 辅助
生成式AI的最新进展允许用户通过文本或图像创建3D模型，但这类模型更注重视觉合理性而非几何精度，常产生存在缺陷的结果，损害其制造后的预期用途。我们提出InstructMesh，这是一款交互式后生成优化工具，支持通过区域选择和针对性操作（如开孔、密封空洞或调整局部厚度）对生成式3D模型进行选择性修复。用户可通过自然语言提示或滑块控件调用编辑操作，InstructMesh直接在中间隐式表示上操作，无需专业建模技能即可实现可靠的几何修正。为指导设计，我们首先分析了最先进生成工具输出中与制造相关的常见失效模式，随后开展两项用户研究，结果表明新手可使用InstructMesh识别并完成生成式输出的制造相关修复，且用户偏好结合滑块控件与自然语言输入的混合界面。
英文摘要
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 fabrication-related failure modes in outputs from state-of-the-art generative tools. We then conduct two user studies, demonstrating that novices can identify and perform fabrication-relevant repairs on generative outputs using InstructMesh, and revealing user preference for hybrid interfaces that combine slider controls with natural language input.
发表机构
- MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
- Google（谷歌公司）
- University of Washington（华盛顿大学）
- Khoury College of Computer Sciences, Northeastern University（东北大学科里计算机科学学院）
- Google XR（谷歌XR部门）
机构由 AI 辅助整理，请以论文原文为准。
