Deep convolutional inverse graphics network
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5851-deep-convolutional-inverse-graphics-network.pdf
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3.81 MB
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Checksum (MD5)
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Author(s)
Kohli, Pushmeet
Kulkarni, Tejas Dattatraya
Whitney, William F.
Tenenbaum, Joshua B
Date Issued
2015
Journal
Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015)
Publisher
Neural Information Processing Systems Foundation, Inc
Citation
Kulkarni, Tejas D. et al. "Deep convolutional inverse graphics network." Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS 2015), December 7-12 2015, Montreal, Canada, Neural Information Processing Systems Foundation, 2015 © 2015 Neural Information Processing Systems Foundation, Inc
Version
Final published version
Abstract
This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene structure and viewing transformations such as depth rotations and lighting variations. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic Gradient Variational Bayes (SGVB) algorithm [10]. We propose a training procedure to encourage neurons in the graphics code layer to represent a specific transformation (e.g. pose or light). Given a single input image, our model can generate new images of the same object with variations in pose and lighting. We present qualitative and quantitative tests of the model's efficacy at learning a 3D rendering engine for varied object classes including faces and chairs.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/5851-deep-convolutional-inverse-graphics-network
