  While recent learning based methods have been observed to be superior for
several vision-related applications, their potential in generating artistic
effects has not been explored much. One such interesting application is Shadow
Art - a unique form of sculptural art where 2D shadows cast by a 3D sculpture
produce artistic effects. In this work, we revisit shadow art using
differentiable rendering based optimization frameworks to obtain the 3D
sculpture from a set of shadow (binary) images and their corresponding
projection information. Specifically, we discuss shape optimization through
voxel as well as mesh-based differentiable renderers. Our choice of using
differentiable rendering for generating shadow art sculptures can be attributed
to its ability to learn the underlying 3D geometry solely from image data, thus
reducing the dependence on 3D ground truth. The qualitative and quantitative
results demonstrate the potential of the proposed framework in generating
complex 3D sculptures that go beyond those seen in contemporary art pieces
using just a set of shadow images as input. Further, we demonstrate the
generation of 3D sculptures to cast shadows of faces, animated movie
characters, and applicability of the framework to sketch-based 3D
reconstruction of underlying shapes.
