Volume 37 (2018)

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https://diglib.eg.org/handle/10.2312/2632118

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Browsing Volume 37 (2018) by Subject "animation"

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    CLUST: Simulating Realistic Crowd Behaviour by Mining Pattern from Crowd Videos
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhao, M.; Cai, W.; Turner, S. J.; Chen, Min and Benes, Bedrich
    In this paper, we present a data‐driven approach to simulate realistic locomotion of virtual pedestrians. We focus on simulating low‐level pedestrians' motion, where a pedestrian's motion is mainly affected by other pedestrians and static obstacles nearby, and the preferred velocities of agents (direction and speed) are obtained from higher level path planning models. Before the simulation, collision avoidance processes (i.e. examples) are extracted from videos to describe how pedestrians avoid collisions, which are then clustered using hierarchical clustering algorithm with a novel distance function to find similar patterns of pedestrians' collision avoidance behaviours. During the simulation, at each time step, the perceived state of each agent is classified into one cluster using a neural network trained before the simulation. A sequence of velocity vectors, representing the agent's future motion, is selected among the examples corresponding to the chosen cluster. The proposed CLUST model is trained and applied to different real‐world datasets to evaluate its generality and effectiveness both qualitatively and quantitatively. The simulation results demonstrate that the proposed model can generate realistic crowd behaviours with comparable computational cost.In this paper, we present a data‐driven approach to simulate realistic locomotion of virtual pedestrians. We focus on simulating low‐level pedestrians' motion, where a pedestrian's motion is mainly affected by other pedestrians and static obstacles nearby, and the preferred velocities of agents (direction and speed) are obtained from higher level path planning models. Before the simulation, collision avoidance processes (i.e. examples) are extracted from videos to describe how pedestrians avoid collisions, which are then clustered using hierarchical clustering algorithm with a novel distance function to find similar patterns of pedestrians' collision avoidance behaviours. During the simulation, at each time step, the perceived state of each agent is classified into one cluster using a neural network trained before the simulation. A sequence of velocity vectors, representing the agent's future motion, is selected among the examples corresponding to the chosen cluster.
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    Direct Position‐Based Solver for Stiff Rods
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Deul, Crispin; Kugelstadt, Tassilo; Weiler, Marcel; Bender, Jan; Chen, Min and Benes, Bedrich
    In this paper, we present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods within the position‐based dynamics (PBD) framework. It is based on the XPBD algorithm, which extends PBD to simulate elastic objects with physically meaningful material parameters. XPBD approximates an implicit Euler integration and solves the system of non‐linear equations using a non‐linear Gauss–Seidel solver. However, this solver requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver to solve the non‐linear equations which significantly improves convergence by solving all constraints of an acyclic structure (tree), simultaneously. Our solver only requires a few Newton iterations to achieve high stiffness and inextensibility. We model inextensible rods and trees using rigid segments connected by constraints. Bending and twisting constraints are derived from the well‐established Cosserat model. The high performance of our solver is demonstrated in highly realistic simulations of rods consisting of multiple 10 000 segments. In summary, our method allows the efficient simulation of stiff rods in the PBD framework with a speedup of two orders of magnitude compared to the original XPBD approach.We present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods. It is based on the XPBD algorithm, which extends Position‐Based Dynamics to simulate elastic objects with physically meaningful material parameters. However, the non‐linear Gauss‐Seidel solver of XPBD requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast, we use Newton iterations in combination with our direct solver which significantly improves convergence by solving all constraints of an acyclic structure simultaneously. We model rods using rigid segments connected by constraints. Bending and twisting constraints are derived from the Cosserat model. The high performance of our solver allows the simulation of rods consisting of multiple 10 000 segments with a speedup of two orders of magnitude compared to the original XPBD approach.
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    An Implicit SPH Formulation for Incompressible Linearly Elastic Solids
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Peer, Andreas; Gissler, Christoph; Band, Stefan; Teschner, Matthias; Chen, Min and Benes, Bedrich
    We propose a novel smoothed particle hydrodynamics (SPH) formulation for deformable solids. Key aspects of our method are implicit elastic forces and an adapted SPH formulation for the deformation gradient that—in contrast to previous work—allows a rotation extraction directly from the SPH deformation gradient. The proposed implicit concept is entirely based on linear formulations. As a linear strain tensor is used, a rotation‐aware computation of the deformation gradient is required. In contrast to existing work, the respective rotation estimation is entirely realized within the SPH concept using a novel formulation with incorporated kernel gradient correction for first‐order consistency. The proposed implicit formulation and the adapted rotation estimation allow for significantly larger time steps and higher stiffness compared to explicit forms. Performance gain factors of up to one hundred are presented. Incompressibility of deformable solids is accounted for with an ISPH pressure solver. This further allows for a pressure‐based boundary handling and a unified processing of deformables interacting with SPH fluids and rigids. Self‐collisions are implicitly handled by the pressure solver.We propose a novel smoothed particle hydrodynamics (SPH) formulation for deformable solids. We propose a novel smoothed particle hydrodynamics (SPH) formulation for deformable solids. Key aspects of our method are implicit elastic forces and an adapted SPH formulation for the deformation gradient that—in contrast to previous work—allows a rotation extraction directly from the SPH deformation gradient. The proposed implicit concept is entirely based on linear formulations. As a linear strain tensor is used, a rotation‐aware computation of the deformation gradient is required. In contrast to existing work, the respective rotation estimation is entirely realized within the SPH concept using a novel formulation with incorporated kernel gradient correction for first‐order consistency.
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    ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation
    (© 2018 The Eurographics Association and John Wiley & Sons Ltd., 2018) Luo, Linbo; Chai, Cheng; Ma, Jianfeng; Zhou, Suiping; Cai, Wentong; Chen, Min and Benes, Bedrich
    How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics.