We introduce a vision based, markerless upper body pose tracking approach that first tracks the 3D movements of extremities, including head and hands. Then based on the knowledge of upper body model, these extremity movements are used to predict the whole upper body motion as an inverse kinematics problem. The experimental validation showed the promise of applying this approach in several smart...

We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classificatio...

Generation and animation of realistic humans is an essential part of many projects in today’s media industry. Especially, the games and special effects industry heavily depend on realistic human animation. In this work a unified model that describes both, human pose and body shape is introduced which allows us to accurately model muscle deformations not only as a function of pose but also depen...

We address the problem of body pose tracking in a scenario of multiple camera setup with the aim of recovering body motion robustly and accurately. The tracking is performed on three-dimensional (3D) space using 3D data, including colored volume and 3D optical flow, which are reconstructed at each time step. We introduce strategies to compute multiple camera-based 3D optical flow and have attai...

For image based body pose estimation, the relationship of body pose and image appearance has often been captured by the means of a geometric body model. In contrast to such handcrafted models, a learned model has many advantages; e.g. it can be learned from training data, and the computations can often be performed parametrically. Furthermore, learned probabilistic models are able to generalise...

The problem we consider in this paper is to take a single two-dimensional image containing a human body, locate the joint positions, and use these to estimate the body configuration and pose in three-dimensional space. The basic approach is to store a number of exemplar 2D views of the human body in a variety of different configurations and viewpoints with respect to the camera. On each of thes...

In this paper we propose a self-initialized method for human pose estimation from multiple cameras. A graphical model for the articulated body is defined through explicit kinematic and structural constraints, which allows for any plausible body configuration and avoids learning the joint distributions from training data. Nonparametric belief propagation (NBP) is used to infer the marginal distr...

In this paper, we present a conceptual design of augmented reality full body interaction based on monocular 3D pose estimation. The proposed design is based on 3D pose estimation from the image of user’s motions captured by a monocular camera and the processing of 3D human poses for augmented reality applications. Based on the method, a 3D human full body model is constructed. The silhouettes e...