We describe twin Gaussian processes (TGP), a generic structured prediction method that
uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and …

We present an algorithm for jointly learning a consistent bidirectional generative-recognition
model that combines top-down and bottom-up processing for monocular 3d human motion …

Many computer vision tasks may be expressed as the problem of learning a mapping
between image space and a parameter space. For example, in human body pose …

We introduce BM³E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent
probabilistic estimates in discriminative visual tracking. The model applies to problems of …

Structured outputs such as multidimensional vectors or graphs are frequently encountered in
real world pattern recognition applications such as computer vision, natural language …

From the streets of London to subway stations in New York City, hundreds of thousands of
surveillance cameras ubiquitously collect hundreds of thousands of videos, often running …

In this paper we study the role of dynamics in dimensionality reduction problems applied to
sequences. We propose a new family of marginal auto-regressive (MAR) models that …

This paper presents a robust computational framework for monocular 3D tracking of human
movement. The main innovation of the proposed framework is to explore the underlying data …

This paper introduces a framework to track 3D human movement using Gaussian process
dynamic model (GPDM) and particle filter. The framework combines the particle filter and …

In this chapter we review challenges and methodology for feature-based predictive three-
dimensional human pose reconstruction, based on image and video data. We argue that …