Optimization-based group performance deducing
Lei Lv, Tianlu Mao, Xuecheng Liu, Zhaoqi Wang
In Computer Animation and Virtual Worlds, 25(2), 2014.
Abstract: Large-scale group performance animation has been an important research topic because of its diverse range of applications including virtual rehearsal and film production. Animating hundreds of virtual actors as what the director wishes is a tough task. In this paper, we address this challenge by introducing an optimization method that generates large-scale group performance by deducing a small-scale one with fewer actors. We introduced group motion bigraph technique and transformed the motion-deducing problem into a constrained optimization problem. A solving process is then presented to automatically obtain the motion of the large group with velocity constraints. Moreover, an interactive system of constructing the group motion bigraph has been implemented, which provides flexible edit and control on deducing group motion. The animation results show that our method is competent for deducing large-scale group performance from only several motion clips performed by small groups.
Keyword(s): large-scale group, group motion bigraph, performance animation
@article{Lv:2014:OGP,
author = {Lei Lv and Tianlu Mao and Xuecheng Liu and Zhaoqi Wang},
title = {Optimization-based group performance deducing},
journal = {Computer Animation and Virtual Worlds},
volume = {25},
number = {2},
pages = {171--184},
year = {2014},
}
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