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A Real-Time Natural Motion Edit by the Uniform Posture Map Algorithm

机译:均匀姿态图算法的实时自然运动编辑

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Many researchers have taken the effort to describe the dynamics of the articulated body by the analytic method. They have obtained excellent results in various fields. However, for the articulated body moving with its voluntary will, it is difficult to generalize the motion pattern by analytical modeling, because the motion pattern is extremely subjective and unpredictable. The learning networks overcome the restriction of analytic modeling through the deductive learning method. The Uniform Posture Map (UPM) is proposed to synthesize a new motion between existing clip motions. It is organized through the quantization of various postures with an unsupervised learning algorithm; it places the output neurons with similar postures in adjacent positions. Using this property, an intermediate posture of applied two postures is generated; the generating posture is used as a key-frame to make an interpolating motion. The UPM needs fewer computational costs, in comparison with other motion transition algorithms. It provides a control parameter; an animator can not only control the motion simply by adjusting this parameter, but also produce animation interactively. The UPM prevents the generating of the invalid output neurons to present unreal postures in the learning phase; thus, it makes more realistic motion curves; finally it contributes to the making of more natural motions.
机译:许多研究人员已努力通过分析方法来描述关节体的动力学。他们在各个领域都取得了优异的成绩。但是,对于以自愿意志运动的关节体,由于运动模式极其主观且不可预测,因此很难通过分析建模来概括运动模式。学习网络通过演绎学习方法克服了分析建模的局限性。提出了统一姿势图(UPM),以在现有剪辑运动之间合成新的运动。它是通过无监督学习算法量化各种姿势而组织的;它将具有相似姿势的输出神经元放置在相邻位置。使用此属性,可以生成应用的两个姿势的中间姿势。生成姿势用作进行插补运动的关键帧。与其他运动转换算法相比,UPM需要更少的计算成本。它提供一个控制参数;动画师不仅可以通过调整该参数来控制运动,而且还可以交互式地制作动画。 UPM可以防止在学习阶段生成无效的输出神经元以呈现不真实的姿势;因此,它使运动曲线更逼真;最后,它有助于做出更自然的动作。

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