Data-Driven Crowds

Crowd Art: Density and Flow Based Crowd Motion Design

Kevin Jordao, Panayiotis Charalambous, Marc Christie, Julien Pettré and Marie-Paule Cani

Inria Rennes-Bretagne Atlantique, Rennes, France        University of Rennes 1 & IRISA, France

University of Grenoble-Alpes, CNRS Laboratory Jean Kuntzmann & Inria Grenoble Rhône-Alpes, France

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Artists, animation and game designers are in demand for solutions to easily populate large virtual environments with crowds that satisfy desired visual features. This paper presents a method to intuitively populate virtual environments by specifying two key features: localized density, being the amount of agents per unit of surface, and localized flow, being the direction in which agents move through a unit of surface. The technique we propose is also time-independant, meaning that whatever the time in the animation, the resulting crowd satisfies both features. To achieve this, our approach relies on the Crowd Patches model. After discretizing the environment into regular patches and creating a graph that links these patches, an iterative optimization process  computes the local changes to apply on each patch (increasing/reducing the number of agents in each patch, updating the directions of agents in the patch) in order to satisfy overall density and flow constraints. A specific stage is then introduced after each iteration to avoid the creation of local loops by using a global pathfinding process. As a result, the method has the capacity of generating large realistic crowds in minutes that endlessly satisfy both user specified densities and flow directions, and is robust to contradictory inputs. At last, to ease the design the method is implemented in an artist-driven tool through a painting interface.

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This is one of our early tests on simulating a massive crowd of > 100 000 characters using as input images.