Data-Driven Crowd Motion Control With Multi-Touch Gestures

Yijun Shen, Joseph Henry, He Wang, Edmond S.L. Ho, Taku Komura, Hubert P.H. Shum

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)382-394
Number of pages13
JournalComputer Graphics Forum
Issue number6
Early online date26 Mar 2018
Publication statusPublished - Sep 2018


  • Animation

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