Deep Disentangled Representations for Volumetric Reconstruction
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2016-08-31
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en
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Abstract
We introduce a convolutional neural network for inferring a
compact disentangled graphical description of objects from 2D images
that can be used for volumetric reconstruction. The network comprises
an encoder and a twin-tailed decoder. The encoder generates a disentangled
graphics code. The first decoder generates a volume, and the second
decoder reconstructs the input image using a novel training regime that
allows the graphics code to learn a separate representation of the 3D
object and a description of its lighting and pose conditions. We demonstrate
this method by generating volumes and disentangled graphical
descriptions from images and videos of faces and chairs.
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Faculteit der Sociale Wetenschappen