In this paper we address the problem of human body pose estimation from still images. A multi-view set of images of a person sitting at a table is acquired and the pose estimated. Reliable and efficient pose estimation from still images represents an important part of more complex algorithms, such as tracking human body pose in a video sequence, where it can be used to automatically initialise ...

Estimating the pose of a rigid body means to determine the rigid body motion in the 3D space from 2D images. For this purpose, it is reasonable to make use of existing knowledge of the object. Our approach exploits the 3D shape and the texture of the tracked object in form of a 3D textured model to establish 3D2D correspondences for pose estimation. While the surface of the 3D free-form model i...

According to the World Health Organization, falling is a significant health problem that causes thousands of deaths every year. Fall detection and fall prediction tasks enable accurate medical assistance vulnerable populations whenever required, allowing local authorities predict daily care resources reduce damages accordingly. We present in this paper, approach explores human body geometry ava...

Lifting the 2D human pose to 3D is an important yet challenging task. Existing estimation suffers from (1) inherent ambiguity between and data, (2) lack of well-labeled 2D–3D pairs in wild. Human beings are able imagine a image or set body key-points with least ambiguity, which should be attributed prior knowledge that we have acquired our mind. Inspired by this, propose new framework leverages...

The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part co...

In this paper we present a novel method for 3D human body shape and pose prediction. Our work is motivated by the need to reduce our reliance on costly-to-obtain ground truth labels. To achieve this, we propose training an encoder-decoder network using a two step procedure as follows. During the first step, a decoder is trained to predict a body silhouette using SMPL [2] (a statistical body sha...

The detection and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes, and the high dimensionality of articulated body models. To cope with these problems we represent the 3D human body as a graphical model in which the relationships between the body parts are represented by conditional probability distributi...