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 fi rst 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.
