Evolution of 3D images from 2D images

To present a model, which will reshape the raw 3D specific single-view images by learning the deformable object categories using unsupervised techniques. Using Convolutional autoencoders, each raw image will be taken as input, then this will be compressed by considering factors such as depth, albedo, viewpoint and illumination of a particular input. Autoencoders are basically used for efficient data codings in an unsupervised manner. These components can disentangled without any labels or supervisor based on the symmetric structure of the object. This model shows the reasoning about illumination which allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, this model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. This model can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, model will be demonstrated with superior accuracy when compared to another method that uses supervision at the level of 2D image correspondences.