Learning to Generate Chairs, Tables and Cars with Convolutional Networks

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Authors Maxim Tatarchenko, Thomas Brox, Jost Tobias Springenberg, Alexey Dosovitskiy
Journal/Conference Name IEEE Transactions on Pattern Analysis and Machine Intelligence
Paper Category
Paper Abstract We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.
Date of publication 2014
Code Programming Language Python
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