  To generalize to novel visual scenes with new viewpoints and new object
poses, a visual system needs representations of the shapes of the parts of an
object that are invariant to changes in viewpoint or pose. 3D graphics
representations disentangle visual factors such as viewpoints and lighting from
object structure in a natural way. It is possible to learn to invert the
process that converts 3D graphics representations into 2D images, provided the
3D graphics representations are available as labels. When only the unlabeled
images are available, however, learning to derender is much harder. We consider
a simple model which is just a set of free floating parts. Each part has its
own relation to the camera and its own triangular mesh which can be deformed to
model the shape of the part. At test time, a neural network looks at a single
image and extracts the shapes of the parts and their relations to the camera.
Each part can be viewed as one head of a multi-headed derenderer. During
training, the extracted parts are used as input to a differentiable 3D renderer
and the reconstruction error is backpropagated to train the neural net. We make
the learning task easier by encouraging the deformations of the part meshes to
be invariant to changes in viewpoint and invariant to the changes in the
relative positions of the parts that occur when the pose of an articulated body
changes. Cerberus, our multi-headed derenderer, outperforms previous methods
for extracting 3D parts from single images without part annotations, and it
does quite well at extracting natural parts of human figures.
