SCA 17: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/2631998

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Browsing SCA 17: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "Computing methodologies"

Now showing 1 - 6 of 6
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    Density Maps for Improved SPH Boundary Handling
    (ACM, 2017) Koschier, Dan; Bender, Jan; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    In this paper, we present the novel concept of density maps for robust handling of static and rigid dynamic boundaries in fluid simulations based on Smoothed Particle Hydrodynamics (SPH). In contrast to the vast majority of existing approaches, we use an implicit discretization for a continuous extension of the density field throughout solid boundaries. Using the novel representation we enhance accuracy and efficiency of density and density gradient evaluations in boundary regions by computationally efficient lookups into our density maps. The map is generated in a preprocessing step and discretizes the density contribution in the boundary's near-field. In consequence of the high regularity of the continuous boundary density field, we use cubic Lagrange polynomials on a narrow-band structure of a regular grid for discretization. This strategy not only removes the necessity to sample boundary surfaces with particles but also decouples the particle size from the number of sample points required to represent the boundary. Moreover, it solves the ever-present problem of particle deficiencies near the boundary. In several comparisons we show that the representation is more accurate than particle samplings, especially for smooth curved boundaries. We further demonstrate that our approach robustly handles scenarios with highly complex boundaries and even outperforms one of the most recent sampling based techniques.
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    Designing Cable-Driven Actuation Networks for Kinematic Chains and Trees
    (ACM, 2017) Megaro, Vittorio; Knoop, Espen; Spielberg, Andrew; Levin, David I.W.; Matusik, Wojciech; Gross, Markus; Thomaszewski, Bernhard; Bächer, Moritz; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    In this paper we present an optimization-based approach for the design of cable-driven kinematic chains and trees. Our system takes as input a hierarchical assembly consisting of rigid links jointed together with hinges. The user also specifies a set of target poses or keyframes using inverse kinematics. Our approach places torsional springs at the joints and computes a cable network that allows us to reproduce the specified target poses. We start with a large set of cables that have randomly chosen routing points and we gradually remove the redundancy. Then we refine the routing points taking into account the path between poses or keyframes in order to further reduce the number of cables and minimize required control forces. We propose a reduced coordinate formulation that links control forces to joint angles and routing points, enabling the co-optimization of a cable network together with the required actuation forces. We demonstrate the efficacy of our technique by designing and fabricating a cable-driven, animated character, an animatronic hand, and a specialized gripper.
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    Evaporation and Condensation of SPH-based Fluids
    (ACM, 2017) Hochstetter, Hendrik; Kolb, Andreas; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    In this paper we present a method to simulate evaporation and condensation of liquids. Therefore, both the air and liquid phases have to be simulated. We use, as a carrier of vapor, a coarse grid for the air phase and mass-preservingly couple it to an SPH-based liquid and rigid body simulation. Since condensation only takes place on rigid surfaces, it is captured using textures that carry water to achieve high surface detail. The textures can exchange water with the air phase and are used to generate new particles due to condensation effects yielding a full two-way coupling of air phase and liquid. In order to allow gradual evaporation and condensation processes, liquid particles can take on variable sizes. Our proposed improved implicit surface definition is able to render dynamic contact angles for moving droplets yielding highly detailed fluid rendering.
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    Inequality Cloth
    (ACM, 2017) Jin, Ning; Lu, Wenlong; Geng, Zhenglin; Fedkiw, Ronald P.; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    As has been noted and discussed by various authors, numerical simulations of deformable bodies often adversely suffer from so-called ''locking'' artifacts. We illustrate that the ''locking'' of out-of-plane bending motion that results from even an edge-spring-only cloth simulation can be quite severe, noting that the typical remedy of softening the elastic model leads to an unwanted rubbery look. We demonstrate that this ''locking'' is due to the well-accepted notion that edge springs in the cloth mesh should preserve their lengths, and instead propose an inequality constraint that stops edges from stretching while allowing for edge compression as a surrogate for bending. Notably, this also allows for the capturing of bending modes at scales smaller than those which could typically be represented by the mesh. Various authors have recently begun to explore optimization frameworks for deformable body simulation, which is particularly germane to our inequality cloth framework. After exploring such approaches, we choose a particular approach and illustrate its feasibility in a number of scenarios including contact, collision, and self-collision. Our results demonstrate the efficacy of the inequality approach when it comes to folding, bending, and wrinkling, especially on coarser meshes, thus opening up a plethora of interesting possibilities.
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    Modeling and Data-Driven Parameter Estimation for Woven Fabrics
    (ACM, 2017) Clyde, David; Teran, Joseph; Tamstorf, Rasmus; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    Accurate estimation of mechanical parameters for simulation of woven fabrics is essential in many fields. To facilitate this we first present a new orthotropic hyperelastic constitutive model for woven fabrics. Next, we design an experimental protocol for characterizing real fabrics based on commercially available tests. Finally, we create a method for accurately fitting the material parameters to the experimental data. The last step is accomplished by solving inverse problems based on a Catmull-Clark subdivision finite element discretization of the Kirchhoff-Love equations for thin shells. Using this approach we are able to reproduce the fully nonlinear behavior corresponding to the captured data with a small number of parameters while maintaining all fundamental invariants from continuum mechanics. The resulting constitutive model can be used with any discretization (e.g., simple triangle meshes) and not just subdivision finite elements. We illustrate the entire process with results for five types of fabric and compare photo reference of the real fabrics to the simulated equivalents.
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    Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
    (ACM, 2017) Laine, Samuli; Karras, Tero; Aila, Timo; Herva, Antti; Saito, Shunsuke; Yu, Ronald; Li, Hao; Lehtinen, Jaakko; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    We present a real-time deep learning framework for video-based facial performance capture-the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character.We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips.