Data-Driven Crowd Motion Control With Multi-Touch Gestures

Abstract

Controlling a crowd using multi-touch devices appeals to the computer games and animation industries, as such devices provide a high-dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre-defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data-driven gesture-based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run-time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run-time control. Our system is accurate and efficient, making it suitable for real-time applications such as real-time strategy games and interactive animation controls.