We explore an approach to 3D people tracking with learned motion models and deterministic optimization. The tracking problem is formulated as the minimization of a differentiable ...
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
Abstract. This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skeletal size from a small set of 2D image features track...
We show that, from the output of a simple 3D human pose tracker one can infer physical attributes (e.g., gender and weight) and aspects of mental state (e.g., happiness or sadness)...
This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a ...