Group motion graphs

Lai, Yu‐Chi; Chenney, Stephen; Fan, Shaohua

doi:10.1145/1073368.1073409

Cited by 48 publications

(34 citation statements)

References 17 publications

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“…Recent work in the animation of group motion has shown that Group Motion Graphs [17] can be used to efficiently control small to mediumsized groups with a small computational load. In [16], using a similar data structure and a mesh-editing algorithm, a method to manipulate existing group motions was presented.…”

Section: Related Workmentioning

confidence: 99%

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Scalable and Robust Shepherding via Deformable Shapes

Harrison

Lien

2010

Motion in Games

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Abstract. In this paper, we present a new motion planning strategy for shepherding in environments containing obstacles. This instance of the group motion control problem is applicable to a wide variety of real life scenarios, such as animal herding simulation, civil crowd control training, and oil-spill cleanup simulation. However, the problem is challenging in terms of scalability and robustness because it is dynamic, highly underactuated, and involves multi-agent coordination. Our previous work showed that high-level probabilistic motion planning algorithms combined with simple shepherding behaviors can be beneficial in situations where low-level behaviors alone are insufficient. However, inconsistent results suggested a need for a method that performs well across a wider range of environments. In this paper, we present a new method, called Deform, in which shepherds view the flock as an abstracted deformable shape. We show that our method is more robust than our previous approach and that it scales more effectively to larger teams of shepherds and larger flocks. We also show Deform to be surprisingly robust despite increasing randomness in the motion of the flock.

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Section: Related Workmentioning

confidence: 99%

“…Recent work in the animation of group motion has shown that Group Motion Graphs [17] can be used to efficiently control small-to medium-sized groups with a small computational load.…”

Section: Related Workmentioning

confidence: 99%

Scalable and Robust Shepherding via Deformable Shapes

Harrison

Lien

2010

Motion in Games

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“…Lai et al [33] proposed a group motion graph that represents the positional configurations among multiple characters. However, the authors did not deal with interactions among the characters.…”

Section: Related Workmentioning

confidence: 99%

Two-Character Motion Analysis and Synthesis

Kwon

Cho

Park³

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we deal with the problem of synthesizing novel motions of standing-up martial arts such as Kickboxing, Karate, and Taekwondo performed by a pair of humanlike characters while reflecting their interactions. Adopting an example-based paradigm, we address three nontrivial issues embedded in this problem: motion modeling, interaction modeling, and motion synthesis. For the first issue, we present a semiautomatic motion-labeling scheme based on force-based motion segmentation and learning-based action classification. We also construct a pair of motion transition graphs, each of which represents an individual motion stream. For the second issue, we propose a scheme for capturing the interactions between two players. A dynamic Bayesian network is adopted to build a motion transition model on top of the coupled motion transition graph that is constructed from an example motion stream. For the last issue, we provide a scheme for synthesizing a novel sequence of coupled motions, guided by the motion transition model. Although the focus of the present work is on martial arts, we believe that the framework of the proposed approach can be conveyed to other two-player motions as well.

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“…Lai et al 8 described another constraint-based system for flock/crowd behavior using group motion graphs. The alternated trajectories are generated by modifying the existing data that is stored in the group motion graph.…”

Section: Previous Workmentioning

confidence: 99%

Autonomous boids

Hartman¹,

Bene??

2006

Computer Animation & Virtual

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The classical work of bird-like objects of Reynolds simulates polarized motion of groups of oriented particles, bird-like objects, or simply boids. To do this, three steering vectors are introduced. Cohesion is the tendency of boids to stay in the center of the flock, alignment smoothes their velocities to similar values, and separation helps them to avoid mutual collisions. If no impetus is introduced the boids wander somewhat randomly so an external leading force is necessary for the correct flock behavior. As can be observed during the bird flocking in the fall, birds sometimes move in a way that is not captured by the above described framework. Some of the birds, typically the ones on the edge of the flock, suddenly shoot-off. The flock then pursues this leader. In the original work by Reynolds the cohesion and separation are two complementary steers. We introduce a complementary force to the alignment that we call the change of leadership. This steer defines the chance of the boid to become a leader and try to escape. The leadership is derived from the boid position and the flock eccentricity. If a boid is on the front edge of the flock it has a higher chance to escape. Escaping from the flock is simulated as a sequence of velocity increases that are added to the current velocity of the boid. The entire system is easy to implement, is efficient, and runs simulations of hundreds of boids on a standard computer at 30 frames per second. Our system is aimed to real-time simulations and has the potential to be used in games, crowd simulations, etc.

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Group motion graphs

Cited by 48 publications

References 17 publications

Scalable and Robust Shepherding via Deformable Shapes

Scalable and Robust Shepherding via Deformable Shapes

Two-Character Motion Analysis and Synthesis

Autonomous boids

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