Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Reactive Pedestrian Path Following from Examples
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
ACM SIGGRAPH 2006 Papers
Controlling individual agents in high-density crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Graphical Models
Crowd Simulation
Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation)
Egocentric affordance fields in pedestrian steering
Proceedings of the 2009 symposium on Interactive 3D graphics and games
ClearPath: highly parallel collision avoidance for multi-agent simulation
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Aggregate dynamics for dense crowd simulation
ACM SIGGRAPH Asia 2009 papers
Data-driven animation of crowds
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
A synthetic-vision based steering approach for crowd simulation
ACM SIGGRAPH 2010 papers
Footstep navigation for dynamic crowds
Computer Animation and Virtual Worlds
Scenario space: characterizing coverage, quality, and failure of steering algorithms
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
| Hi-index | 0.00 |
In terms of computation, steering through a crowd of pedestrians is a challenging task. The problem space is inherently high-dimensional, with each added agent giving yet another set of parameters to consider while finding a solution. Yet in the real world, navigating through a crowd of people is very similar regardless of the population size. The closest people have the most impact while those distant set a more general strategy. To this end, we propose a data-driven system for steering in crowd simulations by splitting the problem space into coarse features for the general world, and fine features for other agents nearby. The system is comprised of a collection of steering contexts, which are qualitatively different overall traffic patterns. Due to their similarity, the scenarios within these contexts have a machine-learned model fit to the data of an offline planner which serves as an oracle for generating synthetic training data. An additional layer of machine-learning is used to select the current context at runtime, and the context's policy consulted for the agent's next step. We experienced speedup from hours per scenario with the offline planner and 10 agents to an interactive framerate of 10FPS for 3,000 agents using our data-driven technique.