… compositional pose regression. It is based on two ideas. First, it uses bones instead of joints
as pose … Second, it exploits the joint connection structure to define a compositional loss …

… compositional pose regression. It is based on two ideas. First, it uses bones instead of joints
as pose … Second, it exploits the joint connection structure to define a compositional loss …

… We extensively evaluate our approach in 2D human pose estimation on five benchmark
datasets. It gets better or comparable accuracy as the state-of-the-art methods on all of them. But …

… compositional nature of DLCMs helps them resolve the ambiguities that appear in bottom-up
pose … to learn the complex compositional patterns within human bodies for pose estimation. …

… In this paper, as [10], we take advantage of Deeply Learned Compositional Model (DLCM)
for human pose estimation. We use key points coding to represent a part and supervise its …

… by 2D-to-3D pose lifting. Despite recent success in 2D HPE, 3D pose regression from 2D …
direction and propose a novel framework, termed Compositional GCN, for 3D HPE. It learns …

… The contributions of our work are the following: first, we present a human pose regression
approach from still images based on the soft-argmax … Compositional human pose regression …

… (4) to the heat map Hk and adopting a regression loss for Jk. We call this approach integral
pose regression. Integral pose regression shares all the merits of both heat map based and …

… We propose a direct, regression-based approach to 2D human pose estimation from single
images. We formulate the problem as a sequence prediction task, which we solve using a …

… In this paper, we propose to perform mutual learning inside the compositional model for
human pose estimation. We conduct inference of the human pose in a compositional model …