  Spectral geometric methods have brought revolutionary changes to the field of
geometry processing -- however, when the data to be processed exhibits severe
partiality, such methods fail to generalize. As a result, there exists a big
performance gap between methods dealing with complete shapes, and methods that
address missing geometry. In this paper, we propose a possible way to fill this
gap. We introduce the first method to compute compositions of non-rigidly
deforming shapes, without requiring to solve first for a dense correspondence
between the given partial shapes. We do so by operating in a purely spectral
domain, where we define a union operation between short sequences of
eigenvalues. Working with eigenvalues allows to deal with unknown
correspondence, different sampling, and different discretization (point clouds
and meshes alike), making this operation especially robust and general. Our
approach is data-driven, and can generalize to isometric and non-isometric
deformations of the surface, as long as these stay within the same semantic
class (e.g., human bodies), as well as to partiality artifacts not seen at
training time.
