Cloning Crowd Motions

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.