Reconstructing 3D human poses from a single 2D image is an ill-posed problem without considering the human body model. Explicitly enforcing physiological constraints is known to be non-convex and usually leads to difficulty in finding an optimal solution. An attractive alternative is to learn a prior model of the human body from a set of human pose data. In this paper, we develop a new approach...

Wenye He Cascaded regression method is a fast and accurate method on finding 2D pose of objects in RGB images. It is able to find the accurate pose of objects in an image by a great number of corrections on the good initial guess of the pose of objects. This paper explains the algorithm and shows the result of two experiments carried by the researchers. The presented new method to quickly and a...

Estimating human pose in static images is challenging due to the high dimensional state space, presence of image clutter and ambiguities of image observations. We present an MCMC framework for estimating 3D human upper body pose. A generative model, comprising of the human articulated structure, shape and clothing models, is used to formulate likelihood measures for evaluating solution candidat...

In the recent years we presented a number of methods for a fully automatic pose estimation [5, 7] and tracking [6] of human bodies in 2D [5] and 3D [6]. Initialization and failure recovery in these methods are facilitated by the use of loose-limbed body model [7] in which limbs are connected via learned probabilistic constraints. The pose estimation and tracking can then be formulated as an inf...

Existing whole-body human pose estimation methods mostly segment the parts of body’s hands and feet for specific processing, which not only splits overall semantics body, but also increases amount calculation complexity model. To address these drawbacks, we designed a novel semantic–structural graph convolutional network (SSGCN) tasks, leverages structure to analyze keypoints through improves a...

In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: 1) a multi-task framework that jointly trains pose regression and body part detectors; 2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large dat...