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arXiv:1411.5928cs.CV

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko, Thomas Brox

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.

arXiv comments
v4: final PAMI version. New architecture figure
Journal reference
DOI
Subjects
cs.CVComputer Vision and Pattern Recognition cs.LGMachine Learning cs.NENeural and Evolutionary Computing

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Discussed in comments2 threads

  1. iGAN – Deep learning software that generates images with a few brushstrokes 1 comment

    • It's possible. But 3D data (3D models, videos) are much more difficult to model via a deep neural net. While most of the researchers focus on modeling 2d images in recent years, there are a few work on 3D. For example, here is a project on modeling 3D objects like chairs and…

      junyanzView on HNParent context

  2. Deep Convolutional Inverse Graphics Network 1 comment

    • Very cool work, I'm happy to see more people thinking about deep networks along these lines. It seems that this is very similar to a recent work put on arxiv back in November, "Learning to Generate Chairs with Convolutional Neural Networks". http://arxiv.org/abs/1411.5928 They…

      riraroboView on HNParent context

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