Three-dimensional (3D) human motion capture is a hot researching topic at present. The network becomes advanced nowadays, the appearance of 3D human motion is indispensable in the multimedia works, such as image, video, and game. 3D human motion plays an important role in the publication and expression of all kinds of medium. How to capture the 3D human motion is the key technology of multimedia product. Therefore, a new algorithm called incremental dimension reduction and projection position optimization (IDRPPO) is proposed in this paper. This algorithm can help to learn sparse 3D human motion samples and generate the new ones. Thus, it can provide the technique for making 3D character animation. By taking advantage of the Gaussian incremental dimension reduction model (GIDRM) and projection position optimization, the proposed algorithm can learn the existing samples and establish the relevant mapping between the low dimensional (LD) data and the high dimensional (HD) data. Finally, the missing frames of input 3D human motion and the other type of 3D human motion can be generated by the IDRPPO.

中文翻译：

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基于增量降维和投影位置优化的人体运动捕捉

三维（3D）人体运动捕捉是当前研究的热点。如今，网络日趋先进，在图像，视频和游戏等多媒体作品中3D人体动作的出现是必不可少的。3D人类运动在各种媒体的发布和表达中起着重要作用。如何捕捉3D人体动作是多媒体产品的关键技术。因此，本文提出了一种新的算法，称为增量维数缩减和投影位置优化（IDRPPO）。该算法可以帮助学习稀疏的3D人体运动样本并生成新的样本。因此，它可以提供用于制作3D角色动画的技术。利用高斯增量降维模型（GIDRM）和投影位置优化，该算法可以学习现有样本，并建立低维（LD）数据和高维（HD）数据之间的映射关系。最后，IDRPPO可以生成输入的3D人体运动和其他类型的3D人体运动的缺失帧。