  We present a new method for 3D shape reconstruction from a single image, in
which a deep neural network directly maps an image to a vector of network
weights. The network \textcolor{black}{parametrized by} these weights
represents a 3D shape by classifying every point in the volume as either within
or outside the shape. The new representation has virtually unlimited capacity
and resolution, and can have an arbitrary topology. Our experiments show that
it leads to more accurate shape inference from a 2D projection than the
existing methods, including voxel-, silhouette-, and mesh-based methods. The
code is available at: https://github.com/gidilittwin/Deep-Meta
