Recent state-of-the-art performance on human-body pose estimation has been achieved
with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include …

This paper proposes a new hybrid architecture that consists of a deep Convolutional Network
and a Markov Random Field. We show how this architecture is successfully applied to the …

State-of-the-art methods for human detection and pose estimation require many training
samples for best performance. While large, manually collected datasets exist, the captured …

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions
involving multi-dimensional arrays efficiently. Since its introduction, it has been one of …

This study evaluates the health risks in megacities in terms of mortality and morbidity due to
air pollution. A new spreadsheet model, Risk of Mortality/Morbidity due to Air Pollution (Ri-…

In this work, we propose a novel and efficient method for articulated human pose estimation
in videos using a convolutional network architecture, which incorporates both color and …

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild
settings due to the lack of 3D annotated data. We propose two anatomically inspired …

This paper introduces a new architecture for human pose estimation using a multi- layer
convolutional network architecture and a modified learning technique that learns low-level …

We present a system for quick and easy manipulation of the body shape and proportions of
a human actor in arbitrary video footage. The approach is based on a morphable model of …

Monocular 3D human-pose estimation from static images is a challenging problem, due to
the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we …