Human pose estimation (HPE) has attracted a significant amount of attention from the
computer vision community in the past decades. Moreover, HPE has been applied to various …

Training accurate 3D human pose estimators requires large amount of 3D ground-truth data
which is costly to collect. Various weakly or self supervised pose estimation methods have …

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 …

Non-rigid object detection and articulated pose estimation are two related and challenging
problems in computer vision. Numerous models have been proposed over the years and …

Abstract Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this paper, we …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

In this work, we address the problem of estimating 2d human pose from still images. Recent
methods that rely on discriminatively trained deformable parts organized in a tree model …

Part-based models have demonstrated their merit in object detection. However, there is a
key issue to be solved on how to integrate the inaccurate scores of part detectors when there …

Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be
tightly clustered both in the configuration space of keypoints, as well as in the appearance …

Cosegmentation is typically defined as the task of jointly segmenting “something similar” in a
given set of images. Existing methods are too generic and so far have not demonstrated …