In this work, we introduce the challenging problem of joint multi-person pose estimation and
tracking of an unknown number of persons in unconstrained videos. Existing methods for …

We propose an efficient approach to exploiting motion information from consecutive frames
of a video sequence to recover the 3D pose of people. Previous approaches typically …

This paper serves as a survey and empirical evaluation of the state-of-the-art in activity
recognition methods using still RGB images and/or videos. Understanding human activities …

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 …

Estimating human pose from videos and image sequences is not only an important
computer vision problem, but also plays very critical role in many real-world applications …

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 …

This paper addresses the problems of both general and also fine-grained human action
recognition in video sequences. Compared with general human actions, fine-grained action …

We propose a personalized ConvNet pose estimator that automatically adapts itself to the
uniqueness of a person's appearance to improve pose estimation in long videos. We make …

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 …

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 …