  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.
