SciELO - Scientific Electronic Library Online

 
vol.17 issue3Analysis of Genetic Expression with Microarrays using GPU Implemented AlgorithmsA Parallel PSO Algorithm for a Watermarking Application on a GPU author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.17 n.3 México Jul./Sep. 2013

 

Artículos

 

GPU Generation of Large Varied Animated Crowds

 

Generación de grandes multitudes animadas y variadas en el GPU

 

Isaac Rudomin1, Benjamín Hernández1,2, Oriam deGyves3, Leonel Toledo3, Ivan Rivalcoba3, and Sergio Ruiz2

 

1 Barcelona Supercomputing Center, Spain. isaac.rudomin@bsc.es,benjamin.hernandez@bsc.es

2 Tecnológico de Monterrey, Campus Ciudad de México, Mexico. sergio.ruiz.loza@itesm.mx

3 Tecnológico de Monterrey, Campus Estado de México, Mexico. A00465730@itesm.mx, ltoledo@itesm.mx, A01167172@itesm.mx

 

Article received on 01/02/2013;
accepted on 10/08/2013.

 

Abstract

We discuss several steps in the process of simulating and visualizing large and varied crowds in real time for consumer-level computers and graphic cards (GPUs). Animating varied crowds using a diversity of models and animations (assets) is complex and costly. One has to use models that are expensive if bought, take a long time to model, and consume too much memory and computing resources. We discuss methods for simulating, generating, animating and rendering crowds of varied aspect and a diversity of behaviors. Efficient simulations run in low cost systems because we use the power of modern programmable GPUs. One can apply similar technology using GPU clusters and HPC for large scale problems. Such systems scale up almost linearly by using multiple nodes. One must combine parallel simulation and parallel rendering in the cluster with interaction and final rendering in lighter clients. However, in view of the latest developments such as the new family of mobile multicore chipsets and GPU-based cloud gaming platforms, the pieces are almost there for this kind of architecture to work.

Keywords: Simulation, real-time crowds, rendering and animation.

 

Resumen

En el artículo se presentan los pasos para simular y visualizar multitudes masivas variadas y animadas en tiempo real, usando el procesador gráfico (GPU). En particular, se discutirán los métodos para la simulación de comportamientos, nivel de detalle, animación y generación de personajes variados. Dada la arquitectura de estas técnicas, se pueden extender a clusters de GPU o en sistemas de cómputo de alto rendimiento (HPC). Estos sistemas son escalables casi linealmente si se incrementa el uso de nodos, sin embargo se deben combinar técnicas de simulación y rendering paralelos. Sin embargo, dados los avances tecnológicos recientes como plataformas de cloud gaming, estas técnicas están listas para funcionar en dichas plataformas.

Palabras clave: Generación, simulación, animación, visualización, multitudes, tiempo real.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Acknowledgements

This work has been partially funded by CONACyT-BSC postdoctoral fellowship; SNI-54067; "Agentes virtuales y Robóticos en Ambientes de Realidad Dual", Tecnológico de Monterrey research initiative. We would also like to thank Nvidia for hardware donations used in some of these algorithms.

 

References

1. Reynolds, C.W. (1987). Flocks, herds and schools: A distributed behavioral model. 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'87), Anaheim, California, 25-34.         [ Links ]

2. Helbing, D. & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical review E, 51 (5), 4282-4286.         [ Links ]

3. Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803), 487-490.         [ Links ]

4. Van Den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multiagent navigation. 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 1928-1935.         [ Links ]

5. Guy, S.J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., & Manocha, D. (2010). PLEdestrians: a least-effort approach to crowd simulation. 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'10), Madrid, Spain, 119-128.         [ Links ]

6. Jund, T., Kraemer, P., & Cazier, D. (2012). A unified structure for crowd simulation. Computer Animation and Virtual Worlds, 23(3-4), 311-320.         [ Links ]

7. Tecchia, F., Loscos, C., Conroy, R., & Chrysanthou, Y. (2001). Agent Behaviour Simulator (ABS): A Platform for Urban Behaviour Development. First International Game Technology Conference. Hong Kong, China.         [ Links ]

8. Treuille, A., Cooper, S., & Popovic, Z. (2006). Continuum crowds. 33rd International Conference and Exhibition on Computer Graphics and Interactive Techniques (ACM SIGGRAPH '06), Boston, USA, 1160-1168.         [ Links ]

9. Millan, E., Hernandez, B., & Rudomin, I. (2007). Large crowds of autonomous animated characters using fragment shaders and level of detail. ShaderX5: Advanced Rendering Techniques (501 -510), Boston, MA: Charles River Media.         [ Links ]

10. Moussaïd, M., Helbing, D., & Theraulaz, G. (2011). How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences of the United States of America, 108(17), 6884-6888.         [ Links ]

11. Ondfej, J., Pettré, J., Olivier, A.H., & Donikian, S. (2010). A synthetic-vision based steering approach for crowd simulation. Special Interest Group on Computer Graphics and Interactive Techniques Conference (SIGGRAPH 2010), Los Angeles, CA., USA, Article No. 123.         [ Links ]

12. Zhou, S., Chen, D., Cai, W., Luo, L., Low, M.Y.H., Tian, F., Tay, V.S.H., Ong, D.W.S., & Hamilton, B.D. (2010). Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation, 20(4), Article 20.         [ Links ]

13. Cormen, T.H., Leiserson, C.E., Rivest, R.L., &Stein, C. (2001). Introduction to Algorithms (2nd ed.). Cambridge, Mass.: MIT Press.         [ Links ]

14. Blue, V.J., Embrechts, M.J., & Adler, J.L. (1997). Cellular automata modeling of pedestrian movements. IEEE International Conference on Systems, Man, and Cybernetics, 3, Orlando, FL, 2320-2323.         [ Links ]

15. Zhang, S., Li, M., Li, F., Liu, A., & Cai, D. (2011). A simulation model of pedestrian flow based on geographical cellular automata. 19th International Conference on Geoinformatics, Shanghai, China, 1 -5.         [ Links ]

16. Zhiqiang, K., Chongchong, Y., Li, T., & Jingyan, W. (2011). Simulation of evacuation based on multi-agent and cellular automaton. International Conference on Mechatronic Science, Electric Engineering and Computer, Jilin, China, 550-553.         [ Links ]

17. Bian, C., Chen, D., & Wang, S. (2010). Velocity field based modelling and simulation of crowd in confrontation operations. 16th International Conference on Parallel and Distributed Systems, Shanghai, China, 646-651.         [ Links ]

18. Pettré, J., Grillon, H., & Thalmann, D. (2008). Crowds of moving objects: Navigation planning and simulation. 35th International Conference and Exhibition on Computer Graphics and Interactive Techniques (SIGGRAPH 2008), Los Angeles, CA, USA, Article No. 54.         [ Links ]

19. Ju, E., Choi, M.G., Park, M., Lee, J., Lee, K.H., & Takahashi, S. (2010). Morphable crowds. ACM Transactions on Graphics, 29(6), Article 40.         [ Links ]

20. Banerjee, B., Abukmail, A., & Kraemer, L. (2008). Advancing the layered approach to agent-based crowd simulation. 22nd Workshop on Principles of Advanced and Distributed Simulation, Roma, Italy, 185-192.         [ Links ]

21. Sukop, M.C. & Thorne Jr., D.T. (2006). Lattice Boltzmann Modeling: An Introduction for Geoscientists and Engineers. Berlin; New York: Springer.         [ Links ]

22. Hernández, B. & Rudomin, I. (2011). A rendering Pipeline for Crowds. GPU Pro 2: Advanced Rendering Techniques (369-384). Natick, Mass.: AK Peters.         [ Links ]

23. Rusinkiewicz, S. & Levoy, M. (2000). QSplat: a multiresolution point rendering system for large meshes. 27th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '00), New Orleans, Louisiana, USA, 343-352.         [ Links ]

24. Blanz, V. & Vetter, T. (1999). A morphable model for the synthesis of 3D faces. 26th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA, 187-194.         [ Links ]

25. Vigueras, G., Lozano, M., Perez, C., & Orduña, J.M. (2008). A Scalable Architecture for Crowd Simulation: Implementing a Parallel Action Serve, 37th International Conference on Parallel Processing, Portland, OR, 430-437.         [ Links ]

26. Vigueras, G., Lozano, M., & Orduña, J.M. (2011). Workload balancing in distributed crowd simulations: the partitioning method. The Journal of Supercomputing, 58(2), 261-269.         [ Links ]

27. Mu, Y., Yan, S., Liu, Y., & Huang, T. (2008). Discriminative local binary patterns for human detection in personal album. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, AK, 1 -8.         [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License