Gaussian Process Dynamical Models for Human Motion

Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario M5S 2E4 Canada.
IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 5.78). 03/2008; 30(2):283-98. DOI: 10.1109/TPAMI.2007.1167
Source: PubMed


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.

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    • "A better solution is to use Gaussian Processes (GPs) which are non-linear, non-parametric models [7]. They have been successfully applied in various tasks including speech and music processing [8] [9] [10]. Previously, we have also used GPs for static music emotion recognition [11]. "

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    • "Motion generation Generation of naturalistic human motion using probabilistic models trained on motion capture data has previous been addressed in the context of computer graphics and machine learning. Prior work has tackled synthesis of stylized human motion using bilinear spatiotemporal basis models [1], Hidden Markov Models [3], linear dynamical systems [21], and Gaussian process latent variable models [46] [40], as well as multilinear variants thereof [12] [45]. Unlike methods based on Gaussian processes, we use a parametric representation and a simple, scalable supervised training method that makes it practical to train on large datasets. "
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    • "Gaussian process regression has been applied to trajectory analysis [11] and human motion modeling [24]. For the multi-object activity modeling, Loy et al. [12] formulated the non-linear relationships between decomposed image regions as a regression problem. "
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