微软研究院的最新大作。 We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body jointsfromasingledepthimage,withoutusinganytemporalinformation.Thekeytobothapproachesistheuseofalarge,realistic, an
We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision
Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DC- NNs). Despite their success on large-scale datasets col- lected in the constrained lab
3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
Abstract
Many approaches try to directly predict 3D pose from image measurements,we explore a simple architecture that reasons through intermediate 2D pose directions.
（1）Deep neural nets
