CHS: Small: Inverse Methods for Computer Graphics Material Appearance Design

从创建增强/虚拟现实(AR/VR)培训环境到设计物理产品，再到指定电影和游戏中的角色和对象的外观，材料外观是视觉设计系统服务于广泛工业需求的关键特征。可靠的培训需要直观的准确性，需要直观的工具将新系统上线所需的时间降至最低。但是，尽管使用计算工具设计物理产品的几何形状已经很成熟，但一个尚未解决的问题是，计算机设计系统中显示的对象的外观在物理生产中无法可靠地匹配。该项目将开发外观设计工具，创建准确的模拟和易于使用。项目成果将产生广泛的影响，通过使系统加快工作培训，通过使面向制造的设计高效，并通过扩大生产工具的获取。本项目将研究外观空间，以创建一种新型的材料外观设计系统。但研究所有材质外观是一个巨大的挑战，因此，不是试图发展一般理论，外观空间将在三个上下文中进行研究：特定材质类的空间；编码为节点图的程序模型空间；以及物理上可实现的双尺度材质的空间。在材料分类的背景下，将通过无监督学习技术收集和分类材料类别(例如，草、闪亮的金属、丝绸)的图像示例，以发现每一类别的结构和外观分类中的重叠。在用于外观设计的节点图的背景下，将收集现有的材质类的图，以发现类型和结构，以驱动节点图自动生成系统的设计。在双尺度材料出现的背景下，将研究中观几何和微尺度变化的类别，以表征它们可以产生的外观范围。这三项研究将被结合在一个系统中，该系统采用远距离和近距离外观的例子，以及用户提供的其他类别限定符，并创建新的节点类型和节点图结构，以产生可调的模型，供用户根据需要进行编辑。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Material appearance is a critical feature of visual design systems in the service of a wide range of industrial needs, from creating augmented/virtual reality (AR/VR) training environments to designing physical products to specifying the appearance of characters and objects in film and games. Visual accuracy is needed for reliable training, and intuitive tools are needed to minimize the time required to bring new systems online. But while design of the geometry of physical products using computational tools is well-established, an unresolved problem is that the appearance of objects shown in computer design systems cannot reliably be matched in physical production. This project will develop tools for appearance design that create accurate simulations and are easy to use. Project outcomes will have broad impact, by enabling systems to accelerate job training, by making design for manufacture efficient, and by expanding access to production tools.This project will study appearance space to create a new type of material appearance design system. But studying the full range of material appearance is a grand challenge, so rather than attempting to develop a general theory, appearance space will be studied in three contexts: the space of particular material classes; the space of procedural models encoded as node graphs; and the space of physically realizable bi-scale materials. In the context of material classes, image examples of classes of materials (e.g., grass, shiny metals, silk fabric) will be gathered and classified by unsupervised learning techniques to discover the structure of each class and the overlap in appearance classifications. In the context of node graphs used for appearance design, existing graphs for material classes will be collected to find types and structures to drive the design of an automatic system for node graph generation. And in the context of bi-scale material appearance, classes of meso-scale geometries and micro-scale variations will be studied to characterize the range of appearance they can produce. The three studies will be brought together in a system that takes examples of distant and close appearance, along with additional user-supplied class qualifiers, and creates new node types and node graph structures to produces a tunable model for the user to edit as needed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.