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ECCV
2010
Springer

Inferring 3D Shapes and Deformations from Single Views

10 years 2 days ago
Inferring 3D Shapes and Deformations from Single Views
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 shape variations, the object phenotype variation and its pose variation using two independent Gaussian Process Latent Variable Models (GPLVMs) respectively. The proposed shape variation models are learnt from 3D samples without prior knowledge about object class, e.g. object parts and skeletons, and are combined to fully span the 3D shape space. A novel probabilistic inference algorithm for 3D shape estimation is proposed by maximum likelihood estimates of the GPLVM latent variables and the camera parameters that best fit generated 3D shapes to given silhouettes. The proposed inference involves a small number of latent variables and it is computationally efficient. Experiments on both human body and shark data demonstrate the efficacy of our new approach.
Added 11 Jul 2010
Updated 11 Jul 2010
Type Conference
Year 2010
Where ECCV
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