  Animals are diverse in shape, but building a deformable shape model for a new
species is not always possible due to the lack of 3D data. We present a method
to capture new species using an articulated template and images of that
species. In this work, we focus mainly on birds. Although birds represent
almost twice the number of species as mammals, no accurate shape model is
available. To capture a novel species, we first fit the articulated template to
each training sample. By disentangling pose and shape, we learn a shape space
that captures variation both among species and within each species from image
evidence. We learn models of multiple species from the CUB dataset, and
contribute new species-specific and multi-species shape models that are useful
for downstream reconstruction tasks. Using a low-dimensional embedding, we show
that our learned 3D shape space better reflects the phylogenetic relationships
among birds than learned perceptual features.
