Human pose estimation refers to the estimation of the location of body parts and how they
are connected in an image. Human pose estimation from monocular images has wide …

We propose a new architecture for the learning of predictive spatio-temporal motion models
from data alone. Our approach, dubbed the Dropout Autoencoder LSTM (DAELSTM), is …

Deep ConvNets have been shown to be effective for the task of human pose estimation from
single images. However, several challenging issues arise in the video-based case such as …

In this work we propose to utilize information about human actions to improve pose
estimation in monocular videos. To this end, we present a pictorial structure model that …

In this paper, we present a method for estimating articulated human poses in videos. We
cast this as an optimization problem defined on body parts with spatio-temporal links …

The MNIST dataset is a popular benchmark dataset in the field of machine learning and
computer vision. The dataset has a training set of 60,000 examples, and a test set of 10,000 …

Abstract We present To The Point (TTP), a method for reconstructing 3D objects from a
single image using 2D to 3D correspondences given only foreground masks, a category …

This paper presents a method to estimate 3D human pose and body shape from monocular
videos. While recent approaches infer the 3D pose from silhouettes and landmarks, we …

In this paper, we present a method to estimate a sequence of human poses in unconstrained
videos. In contrast to the commonly employed graph optimization framework, which is NP …

Monocular 3D reconstruction of articulated object categories is challenging due to the lack of
training data and the inherent ill-posedness of the problem. In this work we use video self …