SteerBug: an interactive framework for specifying and detecting steering behaviors
ABSTRACT The size of crowds that modern computer games and urban simulations are capable of handling has given rise to the challenging problem of debugging and testing massive simulations of autonomous agents. In this paper, we propose SteerBug: an interactive framework for specifying and detecting steering behaviors. Our framework computes a set of time-varying metrics for agents and their environment, which characterize steering behaviors. We identify behaviors of interest by applying conditions (rules) or user defined sketches on the associated metrics. The behaviors we can specify and detect include unnatural steering, plainly incorrect results, or application-specific behaviors of interest. Our framework is extensible and independent of the specifics of any steering approach. To our knowledge, this is the first work that aims to provide a computational framework for specifying and detecting crowd behaviors in animation.
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ABSTRACT: There are very few software frameworks for steering behaviors that are publicly available for developing, evaluating, and sharing steering algorithms. Furthermore, there is no widely accepted methodology for how to evaluate results of agent steering simulations. This situation makes it difficult to identify the real underlying challenges in agent simulations and future research directions to advance the state of the art. With the hope of encouraging community participation to address these issues, we have released SteerSuite, a flexible but easy-to-use set of tools, libraries, and test cases for steering behaviors. The software includes enhanced test cases, an improved version of SteerBench, a modular simulation engine, a novel steering algorithm, and more. Care has been taken to make SteerSuite practical and easy-to-use, yet flexible and forward-looking, to challenge researchers and developers to advance the state of the art in steering.Motion in Games, Second International Workshop, MIG 2009, Zeist, The Netherlands, November 21-24, 2009. Proceedings; 01/2009
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ABSTRACT: In this paper, we propose a new model to quantitatively compare global flow characteristics of two crowds. The proposed approach explores a 4-D histogram that contains information on the local velocity (speed and orientation) of each spatial position, and the comparison is made using histogram distances. The 4-D histogram also allows the comparison of specific characteristics, such as distribution of orientations only, speed only, relative spatial occupancy only, and combinations of such features. Experimental results indicate that the proposed quantitative metric correlates with visual inspection. Copyright © 2012 John Wiley & Sons, Ltd.Computer Animation and Virtual Worlds 02/2012; 23(1):49-57. DOI:10.1002/cav.1423 · 0.42 Impact Factor
Conference Paper: Cloning Crowd Motion[Show abstract] [Hide abstract]
ABSTRACT: This paper introduces a method to clone crowd motion data. Our goal is to efficiently animate large crowds from existing examples of motions of groups of characters by applying an enhanced copy and paste technique on them. Specifically, we address spatial and temporal continuity problems to enable animation of significantly larger crowds than our initial data. We animate many characters from the few examples with no limitation on duration. Moreover, our animation technique answers the needs of real-time applications through a technique of linear complexity. Therefore, it is significantly more efficient than any existing crowd simulation-based technique, and in addition, we ensure a predictable level of realism for animations. We provide virtual population designers and animators with a powerful framework which (i) enables them to clone crowd motion examples while preserving the complexity and the aspect of group motion and (ii) is able to animate large-scale crowds in real-time. Our contribution is the formulation of the cloning problem as a double search problem. Firstly, we search for almost periodic portions of crowd motion data through the available examples. Secondly, we search for almost symmetries between the conditions at the limits of these portions in order to interconnect them. The result of our searches is a set of crowd patches that contain portions of example data that can be used to compose large and endless animations. Through several examples prepared from real crowd motion data, we demonstrate the advantageous properties of our approach as well as identify its potential for future developments.Eurographics/ACM SIGGRAPH Symposium on Computer Animation; 07/2012