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中文摘要：
使用运动捕获数据驱动与动力学模拟相结合的控制方法,可以产生既真实又能对外界施加的作用力作出反应的人体运动.为减少以前方法中反应式运动数据搜索的时间开销并去除动画师需要的手工调节工作,采用并行计算,并引入人工神经网络的方法,根据虚拟人主要关节的位姿对反应运动类型进行预测,得到需搜索的反应运动子类型库.另外,对搜索匹配的算法进行改善以提高搜索效率.实验结果表明：系统中的虚拟人的运动能在两种控制方式之间灵活切换,并能实时响应外界的交互作用.
英文摘要：
By combining motion capture data and dynamic simulation, realistic character animations can be obtained, which can interactively respond to contact forces in the environment. However, the previous character animation generation methods cost so much searching time to find out the appropriate motion capture sequences in the database so that the motion generation process can not run online. Moreover, animators need much manual adjustments to achieve the final realistic character animation. The authors present a parallel algorithm of two processes, and employ an artificial neural network to predict and pre-classify the recovery motion database in order to reduce the size of the search region. The artificial neural network is trained offline by a set of recovery motion capture data sequences and the database is classified according to the recovery motion strategy of the characters. The artificial neural network accepts several key DOFs of the character body segments as input, and outputs the subset label of the recovery motion sequence database as a result. In addition, the matching algorithm is improved for searching for motion capture sequences. The experiment demonstrates that the characters can be controlled and switched between motion capture data and dynamic simulation control modes naturally, and the system can generate reactive virtual character animation in real-time.
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