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 …

Latent variable models (LVM), like the shared-GPLVM and the spectral latent variable
model, help mitigate over-fitting when learning discriminative methods from small or …

Recent research in visual inference from monocular images has shown that discriminatively
trained image-based predictors can provide fast, automatic qualitative 3D reconstructions of …

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

Discriminative approaches to human pose inference involve mapping visual observations to
articulated body configurations. Current probabilistic approaches to learn this mapping have …

Discriminative approaches for human pose estimation model the functional mapping, or
conditional distribution, between image features and 3D pose. Learning such multi-modal …

We propose a statistical formulation for 2D human pose estimation from single images. The
human body configuration is modeled by a Markov network and the estimation problem is to …

A learning based framework is proposed for estimating human body pose from a single
image. Given a differentiable function that maps from pose space to image feature space …

We present an approach for recovering articulated body pose from single monocular images
using the Specialized Mappings Architecture (SMA), a nonlinear supervised learning …

Current approaches to pose estimation and tracking can be classified into two categories:
generative and discriminative. While generative approaches can accurately determine …