ABSTRACT
Simulating multiple character interaction is challenging because character actions must be carefully coordinated to align their spatial locations and synchronized with each other. We present an algorithm to create a dense crowd of virtual characters interacting with each other. The interaction may involve physical contacts, such as hand shaking, hugging, and carrying a heavy object collaboratively. We address the problem by collecting deformable motion patches, each of which describes an episode of multiple interacting characters, and tiling them spatially and temporally. The tiling of motion patches generates a seamless simulation of virtual characters interacting with each other in a nontrivial manner. Our tiling algorithm uses a combination of stochastic sampling and deterministic search to address the discrete and continuous aspects of the tiling problem. Our tiling algorithm made it possible to automatically generate highly complex animation of multiple interacting characters. We achieve the level of interaction complexity far beyond the current state of the art that animation techniques could generate, in terms of the diversity of human behaviors and the spatial/temporal density of interpersonal interactions.
ABSTRACT
In a virtual world, a group of virtual characters can interact with each other, and these characters may leave a group to join another. The interaction among individuals and groups often produces interesting events in a sequence of animation. The goal of this paper is to discover social events involving mutual interactions or group activities in multicharacter animations and automatically plan a smooth camera motion to view interesting events suggested by our system or relevant events specified by a user. Inspired by sociology studies, we borrow the knowledge in Proxemics, social force, and social network analysis to model the dynamic relation among social events and the relation among the participants within each event. By analyzing the variation of relation strength among participants and spatiotemporal correlation among events, we discover salient social events in a motion clip and generate an overview video of these events with smooth camera motion using a simulated annealing optimization method. We tested our approach on different motions performed by multiple characters. Our user study shows that our results are preferred in 66.19 percent of the comparisons with those by the camera control approach without event analysis and are comparable (51.79 percent) to professional results by an artist.
