Sean Curtis

Aggregate dynamics for dense crowd simulation

Large dense crowds show aggregate behavior with reduced individual freedom of movement. We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint called unilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. This approach makes it possible to simulate very large, dense crowds composed of up to a hundred thousand agents at near-interactive rates on desktop computers.

PLEdestrians: a least-effort approach to crowd simulation

We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.

Directing Crowd Simulations Using Navigation Fields

We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents toward desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with the most current local collision avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.

Real-time path planning for virtual agents in dynamic environments

We present a novel approach for real-time path planning of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed from the first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues for accurate computation. Our algorithm is used for real-time multi-agent planning in pursuit-evasion and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal

Fast collision detection for deformable models using representative-triangles

We present a new approach to accelerate collision detection for deformable models. Our formulation applies to all triangulated models and significantly reduces the number of elementary tests between features of the mesh, i.e., vertices, edges and faces. We introduce the notion of Representative-Triangles, standard geometric triangles augmented with mesh feature information and use this representation to achieve better collision query performance. The resulting approach can be combined with bounding volume hierarchies and works well for both inter-object and self-collision detection. We demonstrate the benefit of Representative-Triangles on continuous collision detection for cloth simulation and N-body collision scenarios. We observe up to a one-order of magnitude reduction in feature-pair tests and up to a 5X improvement in query time.

Real-Time Path Planning in Dynamic Virtual Environments Using Multiagent Navigation Graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multiagent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for the local dynamics computation of each agent by extending a social force model [15]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multiagent planning in pursuit-evasion, terrain exploration, and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

Interactive continuous collision detection between deformable models using connectivity-based culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of ldquoprocedural representative trianglesrdquo to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of ldquoorphan setsrdquo to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.

Real-time Path Planning for Virtual Agents in Dynamic Environments

We present a novel approach for real-time path planning of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed from the first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues for accurate computation. Our algorithm is used for real-time multi-agent planning in pursuit-evasion and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal

Wave-based sound propagation in large open scenes using an equivalent source formulation

We present a novel approach for wave-based sound propagation suitable for large, open spaces spanning hundreds of meters, with a small memory footprint. The scene is decomposed into disjoint rigid objects. The free-field acoustic behavior of each object is captured by a compact per-object transfer function relating the amplitudes of a set of incoming equivalent sources to outgoing equivalent sources. Pairwise acoustic interactions between objects are computed analytically to yield compact inter-object transfer functions. The global sound field accounting for all orders of interaction is computed using these transfer functions. The runtime system uses fast summation over the outgoing equivalent source amplitudes for all objects to auralize the sound field for a moving listener in real time. We demonstrate realistic acoustic effects such as diffraction, low-passed sound behind obstructions, focusing, scattering, high-order reflections, and echoes on a variety of scenes.

Virtual Tawaf: A case study in simulating the behavior of dense, heterogeneous crowds

We present a system to simulate the movement of individual agents in large-scale crowds performing the Tawaf. The Tawaf serves as a unique test case. The crowd consists of a heterogeneous set of pilgrims, varying with respect to physical capacity as well as activity. Furthermore, the density of the crowd reaches very high levels. Our approach uses a finite state machine to specify the behavior of the agents at each time step in conjunction with a geometric, agent-based algorithm to specify how an agent interacts with its local neighbors to generate collision-free trajectories. The overall system can model agents with varying age, gender and behaviors, supporting the heterogeniety observed in the performance of the Tawaf, even at high densities.