• News

    :: 2015 CGLab got funding for a TUBITAK research grant on Data Analytics

    :: 2012 CGLab got funding for a bilateral research grant on Augmented Reality together with Università di Bologna.

    :: 2011 CGLab got funding for two Turk Telekom research grants on Augmented Reality.

    :: 2010 Research results will be presented at ISCIS (UK) Conference.

    :: 2010 CGLab got funding for a TUBITAK research grant on Information Visualization

    :: 2010 CGLab member Selcuk Sumengen got TUBITAK fellowship to collaborate with UC Merced.

    :: 2009 Research results will be presented at CGI (Canada), CASA (NL), HCI (USA) and GeoVis (D) Conferences.

    :: 2008 research results were presented at SIGGRAPH, Information Visualization VRCAI, Cyberworlds and CAA 2008 Conferences.

    :: Istanbul Archaeology Museum Marmaray-Metro Rescue Excavations Symposium (in Turkish)

    :: CGLab members received IBM Faculty Award

    :: CGI 2008 will be in Istanbul and co-organized by CG Lab

    :: Three CS 450 'Computing and Art' course projects will be presented at SIGGRAPH'07 and IEEE InfoVis'07 as poster presentations

    :: CG Lab got awarded with a collaboration project on Medical VR and AR with University of Lecce, Italy

    :: A toolkit for cultural heritage researchers,CH-Tools is freely available

    :: Recent papers will be presented at CGI 2007, HCI 2007, Grapp 2007 and CAA 2007

    :: CG Lab got awarded with Sabanci University internal grant for a transdiciplinary project 'Novel Representation and interaction techniques of complex datasets'

    :: CG Lab participated to the exhibition of Techne digital performance platform between 17-22 April, 2006.

    :: CG Lab got funding for three TUBITAK research projects

Crowd Simulation and Information Theory

We have worked on information theoretical approaches to crowd simulation and related areas since 2008. Here are our important papers on this domain.


Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU, Kamer Ali Yuksel, Alp Yucebilgin, Selim Balcisoy, Aytul Ercil, The Visual Computer doi: 10.1007/s00371-012-0718-8

Real-time rendering of large animated crowds consisting of thousands of virtual humans is important for several applications including simulations, games, and interactive walkthroughs but cannot be performed using complex polygonal models at interactive frame rates. For that reason, methods using large numbers of precomputed image-based representations, called impostors, have been proposed. These methods take advantage of existing programmable graphics hardware to compensate for computational expense while maintaining visual fidelity. Thanks to these methods, the number of different virtual humans rendered in real time is no longer restricted by computational power but by texture memory consumed for the variety and discretization of their animations. This work proposes a resource-efficient impostor rendering methodology that employs image morphing techniques to reduce memory consumption while preserving perceptual quality, thus allowing higher diversity or resolution of the rendered crowds. Results of the experiments indicated that the proposed method, in comparison with conventional impostor rendering techniques, can obtain 38 % smoother animations or 87 % better appearance quality by reducing the number of key-frames required for preserving the animation quality via resynthesizing them with up to 92 % similarity on real time.


Integrating information theory in agent-based crowd simulation behavior models, Cagatay Turkay, Emre Koc, Selim Balcisoy, The Computer Journal, doi: 10.1093/comjnl/bxr014

Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent–crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses of the presented behavior model with measured crowd data and two agent-based crowd simulators.


An information theoretic approach to camera control for crowded scenes, Cagatay Turkay, Emre Koc, Selim Balcisoy, The Visual Computer, doi: 10.1007/s00371-009-0337-1

Navigation and monitoring of large and crowded virtual environments is a challenging task and requires intuitive camera control techniques to assist users. In this paper, we present a novel automatic camera control technique providing a scene analysis framework based on information theory. The developed framework contains a probabilistic model of the scene to build entropy and expectancy maps. These maps are utilized to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. After an interest point is chosen, the camera is updated accordingly to display this point. We tested our model in a crowd simulation environment and it performed successfully. Our method can be integrated into existent camera control modules in computer games, crowd simulations and movie pre-visualization applications.

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