Intuitive Crowd Behaviour in Dense Urban Environments using Local Laws
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
Discovering Statistics Using SPSS
Discovering Statistics Using SPSS
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM SIGGRAPH 2006 Papers
Computer Animation and Virtual Worlds
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Real-time navigation of independent agents using adaptive roadmaps
Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Texture Synthesis Based Simulation of Secondary Agents
Motion in Games
Indicative routes for path planning and crowd simulation
Proceedings of the 4th International Conference on Foundations of Digital Games
Evaluating distance metrics for animation blending
Proceedings of the 4th International Conference on Foundations of Digital Games
Experiment-based modeling, simulation and validation of interactions between virtual walkers
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Methodologies for the User Evaluation of the Motion of Virtual Humans
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
A velocity-based approach for simulating human collision avoidance
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
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Realistic simulation of interacting virtual characters is essential in computer games, training and simulation applications. The problem is very challenging since people are accustomed to real-world situations and thus, they can easily detect inconsistencies and artifacts in the simulations. Over the past twenty years several models have been proposed for simulating individuals, groups and crowds of characters. However, little effort has been made to actually understand how humans solve interactions and avoid inter-collisions in real-life. In this paper, we exploit motion capture data to gain more insights into human-human interactions. We propose four measures to describe the collision-avoidance behavior. Based on these measures, we extract simple rules that can be applied on top of existing agent and force based approaches, increasing the realism of the resulting simulations.