  In order to generate novel 3D shapes with machine learning, one must allow
for interpolation. The typical approach for incorporating this creative process
is to interpolate in a learned latent space so as to avoid the problem of
generating unrealistic instances by exploiting the model's learned structure.
The process of the interpolation is supposed to form a semantically smooth
morphing. While this approach is sound for synthesizing realistic media such as
lifelike portraits or new designs for everyday objects, it subjectively fails
to directly model the unexpected, unrealistic, or creative. In this work, we
present a method for learning how to interpolate point clouds. By encoding
prior knowledge about real-world objects, the intermediate forms are both
realistic and unlike any existing forms. We show not only how this method can
be used to generate "creative" point clouds, but how the method can also be
leveraged to generate 3D models suitable for sculpture.
