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

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.