HPRNet: Hierarchical point regression for whole-body human pose estimation

Hierarchical Part-Based Human Body Pose Estimation

This paper addresses the problem of automatic detection and recovery of three-dimensional human body pose from monocular video sequences for HCI applications. We propose a new hierarchical part-based pose estimation method for the upper-body that efficiently searches the high dimensional articulation space. The body is treated as a collection of parts linked in a kinematic structure. Search for...

Metric Regression Forests for Human Pose Estimation

We present a new method for inferring dense data to model correspondences, focusing on the application of human pose estimation from depth images. Recent work proposed the use of regression forests to quickly predict correspondences between depth pixels and points on a 3D human mesh model. That work, however, used a proxy forest training objective based on the classification of depth pixels to ...

WEIWEI GUO, IOANNIS PATRAS: LOKD REGRESSION FOR HUMAN POSE ESTIMATION 1 Learning Output-Kernel-Dependent Regression for Human Pose Estimation

This paper presents a novel method for continuous inter-dependent outputs prediction that predicts each of the multiple output variables based not only on the input but also on the rest of the outputs. We do so by using for each output kernel regression functions that are a convex combination of two kernels: the first kernel defined over the input and the second over the remaining outputs. We p...

Holistic Human Pose Estimation with Regression Forests

In this work, we address the problem of human pose estimation in still images by proposing a holistic model for learning the appearance of the human body from image patches. These patches, which are randomly chosen, are used for extracting features and training a regression forest. During training, a mapping between image features and human poses, defined by joint offsets, is learned; while dur...

Hierarchical 3D Pose Estimation for Articulated Human Body Models from a Sequence of Volume Data