Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

Human pose estimation aims at predicting the poses of human body parts in images or
videos. Since pose motions are often driven by some specific human actions, knowing the …

Given new tasks with very little data---such as new classes in a classification problem or a
domain shift in the input---performance of modern vision systems degrades remarkably …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

We present the first real-time method to capture the full global 3D skeletal pose of a human
in a stable, temporally consistent manner using a single RGB camera. Our method combines …

We present a general and flexible video-level framework for learning action models in
videos. This method, called temporal segment network (TSN), aims to model long-range …

Recent advances in clothes recognition have been driven by the construction of clothes
datasets. Existing datasets are limited in the amount of annotations and are difficult to cope …

In this paper, we propose a novel graph convolutional network architecture, Graph Stacked
Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture …

Our ability to train end-to-end systems for 3D human pose estimation from single images is
currently constrained by the limited availability of 3D annotations for natural images. Most …

Automatic emotion recognition has become a trending research topic in the past decade.
While works based on facial expressions or speech abound, recognizing affect from body …