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Start date: 2010 × Subject: Performance × End date: 2019 ×

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Now showing 1 - 4 of 4
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    Authoring Motion Cycles
    (ACM, 2017) Ciccone, Loïc; Guay, Martin; Nitti, Maurizio; Sumner, Robert W.; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    Motion cycles play an important role in animation production and game development. However, creating motion cycles relies on general-purpose animation packages with complex interfaces that require expert training. Our work explores the speci c challenges of motion cycle authoring and provides a system simple enough for novice animators while maintaining the flexibility of control demanded by experts. Due to their cyclic nature, we show that performance animation provides a natural interface for motion cycle speci cation. Our system allows the user to act several loops of motion using a variety of capture devices and automatically extracts a looping cycle from this potentially noisy input. Motion cycles for di erent character components can be authored in a layered fashion, or our method supports cycle extraction from higher-dimensional data for capture devices that deliver many degrees of freedom. After capture, a custom curve representation and manipulation tool allows the user to coordinate and control spatial and temporal transformations from a single viewport. Ground and other planar contacts are speci ed with a single sketched line that adjusts a curve's position and timing to establish non-slipping contact. We evaluate the e ectiveness of our work through tests with both novice and expert users and show a variety of animated motion cycles created with our system.
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    RPI-MATLAB-Simulator: A Tool for Efficient Research and Practical Teaching in Multibody Dynamics
    (The Eurographics Association, 2013) Williams, Jedediyah; Lu, Ying; Niebe, Sarah; Andersen, Michael; Erleben, Kenny; Trinkle, Jeffrey C.; Jan Bender and Jeremie Dequidt and Christian Duriez and Gabriel Zachmann
    We present the RPI-MATLAB-Simulator (RPIsim) as an open source tool for research and education in multibody dynamics. RPIsim is designed and organized to be extended. Its modular design allows users to edit or add new components without worrying about extra implementation details. RPIsim has two main goals: 1. Provide an intuitive and easily extendable platform for research and education in multibody dynamics. 2. Maintain an evolving code base of useful algorithms and analysis tools for multibody dynamics problems. Although research often focuses on a specific subset of problems, work too often begins with developing software in a broader scope simply to realize a test bed for research to begin. It is our hope that RPIsim alleviates some of this burden by decreasing development time, thusly increasing efficiency in research. Further, we aim to provide a practical teaching tool. Because it is a fully working simulator, and since it offers the instant gratification of visualized contact dynamics, RPIsim offers students the opportunity to experiment and explore dynamics in the powerful environment of MATLAB. With multiple built-in simulation methods, and support for a simulation data convention, RPIsim facilitates the fair comparison of methods, including those being developed with RPIsim.
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    Highly Efficient Controlled Hierarchical Data Reduction techniques for Interactive Visualization of Massive Simulation Data
    (The Eurographics Association, 2019) Dubois, Jérôme; Lekien, Jacques-Bernard; Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta
    With the constant increase in compute power of supercomputers, high performance computing simulations are producing higher fidelity results and possibly massive amounts of data. To keep visualization of such results interactive, existing techniques such as Adaptive Mesh Refinement (AMR) can be of use. In particular, Tree-Based AMR methods (TB-AMR) are widespread in simulations and are becoming more present in general purpose visualization pipelines such as VTK. In this work, we show how TB-AMR data structures could lead to more efficient exploration of massive data sets in the Exascale era. We discuss how algorithms (filters) should be designed to take advantage of tree-like data structures for both data filtering or rendering. By introducing controlled hierarchical data reduction we greatly reduce the processing time for existing algorithms, sometimes with no visual impact, and drastically decrease exploration time for analysts. Also thanks to the techniques and implementations we propose, visualization of very large data is made possible on very constrained resources. These ideas are illustrated on million to billion-scale native TB-AMR or resampled meshes, with the HyperTreeGrid object and associated filters we have recently optimized and made available in the Visualisation Toolkit (VTK) for use by the scientific community.
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    Fast Mesh Validation in Combustion Simulations through In-Situ Visualization
    (The Eurographics Association, 2019) Shudler, Sergei; Ferrier, Nicola; Insley, Joseph; Papka, Michael E.; Patel, Saumil; Rizzi, Silvio; Childs, Hank and Frey, Steffen
    In-situ visualization and analysis is a powerful concept that aims to give users the ability to process data while it is still resident in memory, thereby vastly reducing the amount of data left for post-hoc analysis. The problem of having too much data for posthoc analysis is exacerbated in large-scale high-performance computing applications such as Nek5000, a massively-parallel CFD (Computational Fluid Dynamics) code used primarily for thermal hydraulics problems. Specifically, one problem users of Nek5000 often face is validating the mesh, that is identifying the exact location of problematic mesh elements within the whole mesh. Employing the standard post-hoc approach to address this problem is both time consuming and requires vast storage space. In this paper, we demonstrate how in-situ visualization, produced with SENSEI, a generic in-situ platform, helps users quickly validate the mesh. We also provide a bridge between Nek5000 and SENSEI that enables users to use any existing and future analysis routines in SENSEI. The approach is evaluated on a number of realistic datasets.