Reducing Memory Requirements for Diverse Animated Crowds

Abstract

Large-scale crowd simulation and visualization is crucial for the next generation of interactive virtual environments. Current authoring techniques produce good results but are laborious, and demand valuable graphics memory and computational resources beyond the reach of consumer-level hardware. In this paper, we propose a technique for generating animatable characters for crowds that reduces memory requirements. The first step consists in reducing, segmenting and labeling a data set of virtual characters into simpler body parts; labeling information is then manually generated and used to correctly match different body parts in order to generate new characters. The second step comprises a method to embed the rig and skinning information into the texture space shared among the new characters. Additional methods using color, skin features, pattern, fat, wrinkle and textile fold maps are used to add more variety. Animation sequences are stored in auxiliary textures. These can be transferred between different characters, as well as versions of the same characters with different level of detail; such animations can be modified, and otherwise reused, increasing variety yet reducing memory requirements. We will demonstrate that our technique has four advantages: first, memory requirements are reduced by 91% when compared to traditional libraries; second, it can also generate previously nonexistent characters from the original data set; third, embedding the rig and skinning into texture space allows painless animation transfer between different characters and fourth, between different levels of detail of the same characters.