论文标题

地理标准:可解释的形状程序

GeoCode: Interpretable Shape Programs

论文作者

Pearl, Ofek, Lang, Itai, Hu, Yuhua, Yeh, Raymond A., Hanocka, Rana

论文摘要

能够轻松,直观地生成结构上有效的3D形状的程序程序的任务仍然是计算机视觉和图形中的难以捉摸的目标。在图形社区中,生成过程3D模型已转移到使用节点图系统。它们允许艺术家通过视觉编程创建复杂的形状和动画。作为高级设计工具,他们使程序3D建模更容易访问。但是,制作这些节点图需要专业知识和培训。我们提出了Geocode,这是一个旨在扩展现有节点图系统的新型框架,并显着降低了创建新程序3D形状程序的标准。我们的方法可以很好地平衡基于部分形状的表现力和概括。我们提出了一组策划的新几何构建块,这些构建块在跨域中具有表现力和可重复使用。我们展示了通过我们的技术和几何构建块制定的三个创新和表现力的程序。我们的程序执行复杂的规则,使用户有能力执行直观的高级参数编辑,这些参数编辑,这些参数在整个形状上无缝地在较低级别上传播,同时保持其有效性。为了评估非专家之间的几何构建块的用户友好性,我们进行了一项用户研究,证明了它们的易用性并突出了它们在不同领域的适用性。经验证据表明,与现有竞争对手相比,地理码在推断和恢复3D形状方面的卓越精度。此外,与利用粗音原始物质的替代方案相比,我们的方法表现出较高的表现力。值得注意的是,我们说明了执行可控制的本地和全球形状操作的能力。

The task of crafting procedural programs capable of generating structurally valid 3D shapes easily and intuitively remains an elusive goal in computer vision and graphics. Within the graphics community, generating procedural 3D models has shifted to using node graph systems. They allow the artist to create complex shapes and animations through visual programming. Being a high-level design tool, they made procedural 3D modeling more accessible. However, crafting those node graphs demands expertise and training. We present GeoCode, a novel framework designed to extend an existing node graph system and significantly lower the bar for the creation of new procedural 3D shape programs. Our approach meticulously balances expressiveness and generalization for part-based shapes. We propose a curated set of new geometric building blocks that are expressive and reusable across domains. We showcase three innovative and expressive programs developed through our technique and geometric building blocks. Our programs enforce intricate rules, empowering users to execute intuitive high-level parameter edits that seamlessly propagate throughout the entire shape at a lower level while maintaining its validity. To evaluate the user-friendliness of our geometric building blocks among non-experts, we conducted a user study that demonstrates their ease of use and highlights their applicability across diverse domains. Empirical evidence shows the superior accuracy of GeoCode in inferring and recovering 3D shapes compared to an existing competitor. Furthermore, our method demonstrates superior expressiveness compared to alternatives that utilize coarse primitives. Notably, we illustrate the ability to execute controllable local and global shape manipulations.

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