Correction to "Gaussian Process Dynamical Models for Human Motion"

نویسندگان

  • Jack M. Wang
  • David J. Fleet
  • Aaron Hertzmann
چکیده

(a) (b) (c) Fig. 7. Models learned with fixed ¯ α from three different walking subjects. (a) The learned latent coordinates shown in blue. (b) − ln variance plot shows smooth high confidence regions, but the variance near data is larger than in Fig.5c, similar to B-GPDM. (c) Typical samples from the dynamic predictive distribution are shown in green, while the mean-prediction sample is shown in red. (a) (b) (c) Fig. 8. Models learned with two-stage MAP from four different walking subjects. (a) The learned latent coordinates shown in blue, note the walkers are separated into distinct portions of the latent space. (b) − ln variance plot shows smooth high confidence regions, and the variance near data is similar to Fig.5c. (c) Typical samples from the dynamic predictive distribution are shown in green, while the mean-prediction sample is shown in red.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Gaussian Process Dynamical Models

This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM 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, which amounts to using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This re...

متن کامل

Coupling Gaussian Process Dynamical Models with Product-of-Experts Kernels

We describe a new probabilistic model for learning of coupled dynamical systems in latent state spaces. The coupling is achieved by combining predictions from several Gaussian process dynamical models in a product-of-experts fashion. Our approach facilitates modulation of coupling strengths without the need for computationally expensive re-learning of the dynamical models. We demonstrate the ef...

متن کامل

3d Human Tracking with Gaussian Process Annealed

We present an approach for tracking human body parts with prelearned motion models in 3D using multiple cameras. We use an annealed particle filter to track the body parts and a Gaussian Process Dynamical Model in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. We also present an improvement for the weighting function that helps t...

متن کامل

Gaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling

Time series data of high dimensions are frequently encountered in fields like robotics, computer vision, economics and motion capture. In this survey paper we look first at Gaussian Process Latent Variable Model (GPLVM) which is a probabilistic nonlinear dimensionality reduction method. Further we discuss Gaussian Process Dynamical Model (GPDMs) which are based GPLVM. GPDM is a probabilistic ap...

متن کامل

Variational Gaussian Process Dynamical Systems

High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the variational Gaussian process dynamical system. Our work builds on recent varia...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • IEEE Trans. Pattern Anal. Mach. Intell.

دوره 30  شماره 

صفحات  -

تاریخ انتشار 2008