Wang Jack M, Fleet David J, Hertzmann Aaron
Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario M5S 2E4 Canada.
IEEE Trans Pattern Anal Mach Intell. 2008 Feb;30(2):283-98. doi: 10.1109/TPAMI.2007.1167.
We introduce Gaussian process dynamical models (GPDM) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensionalmotion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian process priors for both the dynamics and the observation mappings. This results in a non-parametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach, and compare four learning algorithms on human motion capture data in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
我们引入高斯过程动态模型（GPDM）用于非线性时间序列分析，并将其应用于从高维运动捕捉数据中学习人体姿势和运动模型。GPDM是一种潜在变量模型。它由一个具有相关动态的低维潜在空间以及从潜在空间到观测空间的映射组成。我们使用高斯过程先验对动态和观测映射进行封闭形式的模型参数边缘化。这产生了一个用于动态系统的非参数模型，该模型考虑了模型中的不确定性。我们展示了该方法，并在每个姿势为50维的人体运动捕捉数据上比较了四种学习算法。尽管使用的数据集较小，但GPDM在这些空间中学习到了非线性动态的有效表示。
