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 present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure" model. The 3D shape of an object class is represented by sets...
Kristen Grauman, Gregory Shakhnarovich, Trevor Dar...
In this paper we investigate shape and motion retrieval in the context of multi-camera systems. We propose a new lowlevel analysis based on latent silhouette cues, particularly su...
Li Guan, Jean-Sebastien Franco, Edmond Boyer, Marc...
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor pri...
In this paper we propose a 3D reconstruction algorithm by combining shape from silhouette with stereo. Visual hull of the object is first derived from multi-view silhouette image...