This paper focuses on real-time markerless motion capture. The body pose of a person is estimated from depth images using an Iterative Closest Point algorithm. We present a very efficient approach, that estimates up to 28 degrees of freedom from 1000 data points with 4Hz. This is achieved by nonlinear optimization techniques using an analytically derived Jacobian and highly optimized correspond...

Semantic action recognition aims to classify actions based on the associated semantics, which can be applied in video captioning and human-machine interaction. In this paper problem is addressed by jointly learning multiple pose lexicons body parts. Specifically, visual models are learnt, one model with part, characterises likelihood of an observed frame being generated from hidden poses. Moreo...

Rapid and accurate estimation of a person’s upper body shape and real-time tracking of the pose in the presence of occlusions is crucial for many future assistive technologies, health care applications and telemedicine systems. We propose to tackle this challenging problem by combining data-driven and generative methods for both body shape and pose estimation. Our strategy comprises a subspace-...

In this paper, we present a new bottom-up one-stage method for whole-body pose estimation, which call “hierarchical point regression,” or HPRNet short. standard body the locations of ~17 major joints on human are estimated. Differently, in fine-grained keypoints (68 face, 21 each hand and 3 foot) estimated as well, creates scale variance problem that needs to be addressed. To handle among diffe...