CHS: Medium: Collaborative Research: Integrated Simulation of Cloth Mechanics and Appearance for Predictive Virtual Prototyping

仿真虚拟样机在许多领域的设计过程中消除了许多试错。但要成为一个有用的虚拟原型，模拟必须可靠地预测被设计物体的相关特性：桥梁的强度；飞机的升力，或织物的外观。先前渲染技术的进步导致了当前渲染图像的爆炸式增长，这些图像被用来制作物体外观的原型，甚至可以向客户展示它们，但前提是所涉及的材料可以很好地表示出来。纺织品目前很难表现，因为它们复杂的结构和微妙的反射，因为它们的外观与它们的形状和运动密不可分。虽然相当适用于电影，但目前的技术水平模拟布料的外观和变形并不能预测真实的外观，因此不能用于设计过程，在设计过程中，真实但不准确的结果是无用的。这个项目代表了康奈尔大学的计算机科学家和罗德岛设计学院的纺织品设计师之间的合作努力，旨在创建所需的预测模拟工具，使虚拟原型能够彻底改变纺织和服装行业，就像它已经彻底改变了许多制造业领域一样。康奈尔大学团队创建的新模型和算法将被集成到用于织物设计的Loomit工具中，然后RISD团队将使用该工具生产新的纺织品，这些纺织品将被运回康奈尔大学，在那里将研究由Loomit增强的功能导致的RISD设计过程的变化。在罗德岛设计学院，这种参与将吸引许多学生艺术家和设计师，帮助他们在未来的职业生涯中发展与越来越多的技术媒体合作的技能，而在康奈尔大学，该项目将为计算机科学专业的学生提供理解和解决艺术家和设计师面临的问题的经验。由于许多基本问题尚未解决，纺织品的外观模拟仍然是一个挑战。最先进的逼真布料渲染和模拟模型是为娱乐应用而开发的，不能准确预测真实纺织品的行为。在这个项目中，PI团队将把外观和变形模型与CT、成像和其他模式的纺织品测量结果进行比较，然后根据需要对它们进行改进，使它们能够适应真实的材料。目前的方法不能很好地捕捉现有织物的特性，无法在近距离目视检查下预测其外观。PI团队将开发新方法来共同捕获纺织品的外观和机械性能，将测量和模型调整相结合，以产生纺织品及其制成的服装和家具的预测工作模型。详细的布料模拟和渲染目前是不切实际的中等复杂的对象，如完整的服装。可伸缩性基本上需要多尺度模型，这些模型只在实际需要时才模拟细节。PI团队将创建多尺度模型，在设计过程中实现交互式、预测性可视化。由于目标应用程序都需要准确的预测，因此研究的主要焦点是验证，测试单个技术部分的准确性和新技术在实际使用中的最终有用性。

Simulated virtual prototypes have removed much trial-and-error from the design process in many fields. But to be useful as a virtual prototype, a simulation must reliably predict the relevant characteristics of the thing being designed: strength for a bridge; lift for an aircraft, or appearance for a fabric. Previous advances in rendering have led to a current explosion in the use of rendered images to prototype the appearance of objects, and even to represent them to customers, but only where the materials involved can be represented well. Textiles are currently difficult to represent, due to their intricate construction and subtle reflectance, and because their appearance is inextricably linked to their shape and motion. Although fairly suitable for movies, the state of the art in simulating cloth appearance and deformation is not predictive of real appearance and therefore not usable in a design process, in which realistic but inaccurate results are useless. This project, which represents a collaborative effort between computer scientists at Cornell University and textile designers at the Rhode Island School of Design, aims to create the predictive simulation tools needed to enable virtual prototyping to revolutionize the textile and garment industries in the same way it has already revolutionized so many fields of manufacturing. New models and algorithms created by the Cornell team will be integrated into the Loomit tool for fabric design, which will then be used by the RISD team to produce new textiles that will in turn be shipped back to Cornell where changes in the RISD design process resulting from Loomit's enhanced capabilities will be studied. At RISD this involvement will engage many student artists and designers, helping them develop the skills to work with increasingly technological media in their future careers, while at Cornell the project will afford computer science students experience understanding and solving problems faced by artists and designers.Textiles remain a challenge for appearance simulation because of many unsolved fundamental problems. The state-of-the-art models for realistic cloth rendering and simulation were developed for entertainment applications and do not accurately predict the behavior of real textiles. In this project, the PI team will compare appearance and deformation models to measurements of textiles from CT, imaging, and other modalities, then improve them as needed so that they are capable of fitting real materials. Current methods cannot capture the properties of an existing fabric well enough to predict its appearance under close visual inspection. The PI team will develop new methods for jointly capturing the appearance and mechanical properties of textiles, combining measurement and model tuning to produce predictive working models of textiles and the garments and furnishings made from them. Detailed cloth simulations and renderings are currently impractical for moderately complex objects, such as complete garments. Scalability fundamentally requires multi-scale models that simulate details only when they are actually needed. The PI team will create multi-scale models that enable interactive, predictive visualization during the design process. Since the targeted applications all require accurate prediction, a major focus of the research is on validation, testing both the accuracy of the individual technical pieces and the ultimate usefulness of the new techniques in actual use.