CRII: CHS: Capturing Emergent Fine-Scale Features in Visual Simulation of Elasticity

CRII：CHS：捕捉弹性视觉模拟中出现的精细尺度特征

• 批准号: 1657089
• 负责人: Stephen Guy
• 金额: $ 17.5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657089&HistoricalAwards=false
• 关键词: CRII; CHS; Capturing; Emergent; Fine

Visual simulation of natural phenomena is an established tool in digital animation and is also growing in importance in design and training applications. Interactive simulation of elastic deformable objects such as fabric and soft tissue, in particular, has a direct benefit for apparel design, e-commerce, and medical training, where it can enable efficient and accurate previewing of garment designs or surgical actions. But for simulation techniques to be truly valuable in these applications (so that they influence real life decisions), they must be visually representative of the behavior of the real phenomenon, including emergent fine-scale features such as wrinkles and folds, which must be reliably and faithfully reproduced as they convey information about the underlying physical properties of the material. Because direct high-resolution simulation is too expensive for visual applications, many techniques have been developed to represent the details efficiently, including adaptive refinement and procedural synthesis. Both types of methods depend crucially on being able to determine whether the current simulation resolution is inadequate to resolve the fine-scale detail that should exist in the system. To do so, one must predict when such detail is likely to emerge, and how it will evolve over time. Unfortunately, existing criteria for doing this rely either on expensive a posteriori error estimation, or on heuristic approaches requiring careful manual parameter tuning to generate reliable results. This research aims to make progress towards the goal of robust and versatile physics-based techniques that can accurately predict the formation of unresolved fine-scale features and characterize their behavior as the simulation proceeds. The versatile framework for detail prediction developed in this work will make visual simulation more efficient, reliable, and representative of the true behavior of the depicted phenomenon, stimulating its adoption in applications such as apparel design and surgical planning and training. The PI will transfer project outcomes to practitioners in both these fields through collaboration with design faculty and surgeons in his University; this will additionally train computer science students in interdisciplinary research through interactions with experts in design and surgery. Applications of this work in visual arts, design, and medicine will be used for outreach, and to engage art and design students who would not otherwise be attracted to STEM fields.This project will address a fundamental challenge in numerical simulation: how can one efficiently characterize unresolved detail in a simulated physical system? Previous work by the PI has pointed to dynamical instabilities as an essential criterion to predict the emergence of fine-scale features. This work will build a theoretical and computational framework that formalizes this insight, and provides efficient and practical techniques for capturing emergent detail in the simulation of deformable objects. The contribution of the work will be a framework of novel numerical methods that can automatically detect and characterize dynamical instabilities in a general setting. Doing so will extend the versatility of a priori refinement techniques, making them applicable to a broad class of elastic simulation problems. The proposed methods will be evaluated on practical problems arising in applications such as garment design and virtual surgery. The knowledge gained from this work will advance understanding of quantitative and qualitative fidelity in simulations of dynamical systems, and will provide a sound theoretical foundation for future work on adaptive simulation.

自然现象的视觉模拟是数字动画中的既定工具，在设计和培训应用中也越来越重要。 弹性可变形物体（例如织物和软组织）的交互式模拟尤其对服装设计、电子商务和医疗培训具有直接益处，其中它可以实现服装设计或手术动作的高效且准确的预览。 但是，为了使模拟技术在这些应用中真正有价值（从而影响真实的生活决策），它们必须在视觉上代表真实的现象的行为，包括涌现的精细尺度特征，如皱纹和褶皱，这些特征必须可靠而忠实地再现，因为它们传达了有关材料基本物理特性的信息。 由于直接的高分辨率模拟对于视觉应用来说太昂贵，因此已经开发了许多技术来有效地表示细节，包括自适应细化和过程合成。 这两种类型的方法都依赖于能够确定当前的模拟分辨率是否不足以解决系统中应该存在的精细尺度细节。 要做到这一点，人们必须预测这些细节何时可能出现，以及它将如何随着时间的推移而演变。 不幸的是，现有的标准，这样做依赖于昂贵的后验误差估计，或启发式的方法，需要仔细的手动参数调整，以产生可靠的结果。 这项研究旨在朝着稳健和通用的基于物理的技术的目标取得进展，这些技术可以准确预测未解决的精细尺度特征的形成，并在模拟过程中表征其行为。 这项工作中开发的多功能细节预测框架将使视觉模拟更加有效，可靠，并代表所描绘现象的真实行为，刺激其在服装设计和手术规划和培训等应用中的采用。 PI将通过与大学的设计学院和外科医生合作，将项目成果转移给这两个领域的从业者;这将通过与设计和外科专家的互动，在跨学科研究中培养计算机科学学生。 这项工作在视觉艺术，设计和医学的应用将用于推广，并吸引艺术和设计专业的学生，否则他们不会被吸引到STEM领域。这个项目将解决数值模拟中的一个基本挑战：如何有效地描述模拟物理系统中未解决的细节？ PI以前的工作指出，动力学不稳定性是预测细尺度特征出现的一个重要标准。 这项工作将建立一个理论和计算框架，形式化这一见解，并提供有效和实用的技术，捕捉新兴的细节在模拟可变形物体。 这项工作的贡献将是一个框架的新的数值方法，可以自动检测和表征动力学不稳定性的一般设置。 这样做将扩展先验细化技术的多功能性，使它们适用于广泛的弹性模拟问题。 所提出的方法将在服装设计和虚拟手术等应用中出现的实际问题进行评估。 从这项工作中获得的知识将促进了解定量和定性保真度的动态系统的模拟，并将提供一个良好的理论基础，为今后的工作自适应模拟。

项目成果

Accurate dissipative forces in optimization integrators

• DOI: 10.1145/3272127.3275011
• 发表时间: 2018-12
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: George E. Brown;Matthew Overby;Zahra Forootaninia;Rahul Narain