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Annotated dynamic texture graph

机译:带注释的动态纹理图

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摘要

We introduce annotated dynamic texture graph (ADTG) for nonlinear motion synthesis, with applications to learning models of human pose and motion from capture data. Our method is based on clustering the motion data into motion primitives that capture local dynamical characteristics - dynamic texture, modeling the dynamics in each cluster using linear dynamic system (LDS), annotating those LDS' which have clear meaning and calculating the cross-entropy between frames of LDS' to construct a directed graph which has two-level structure. The lower level retain the detail and nuance of live motion, while the higher level generalizes motion and encapsulates connections among LDS'. Our results show that this framework can generates lifelike, controllable motion in interactive environments.
机译:我们介绍了用于非线性运动合成的带注释的动态纹理图(ADTG),并将其应用于从捕获数据中学习人体姿势和运动的模型。我们的方法基于将运动数据聚类为运动图元,以捕获局部动态特征-动态纹理,使用线性动态系统(LDS)对每个聚类中的动力学建模,注释那些具有明确含义的LDS',并计算之间的交叉熵LDS'框架以构造具有两级结构的有向图。较低的级别保留了实时运动的细节和细微差别,而较高的级别则概括了运动并封装了LDS'之间的连接。我们的结果表明,该框架可以在交互式环境中生成逼真的,可控制的运动。

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