Non-rigid Structure from Motion with Diffusion Maps Prior
In this paper, a novel approach based on a non-linear manifold learning technique is proposed to recover 3D nonrigid structures from 2D image sequences captured by a single camera. Most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These techniques perform well when the deformations are relatively small or simple, but fail when more complex deformations need to be recovered. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. A specific type of shape variations might be governed by only a small number of parameters, therefore can be wellrepresented in a low dimensional manifold. We learn a nonlinear shape prior using diffusion maps method. The key
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