Abstract. In this paper we propose a probabilistic framework that models shape variations and infers dense and detailed 3D shapes from a single silhouette. We model two types of sh...
We consider the problem of monocular 3d body pose tracking from video sequences. This task is inherently ambiguous. We propose to learn a generative model of the relationship of bo...
Tobias Jaeggli, Esther Koller-Meier, Luc J. Van Go...
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)...
In this paper an approach to recover the 3D human body pose from static images is proposed. We adopt a discriminative learning technique to directly infer the 3D pose from appearan...
Suman Sedai, Farid Flitti, Mohammed Bennamoun, Du ...
We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM) [1]...
Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrenc...