Unsupervised Representation Learning for Diverse Deformable Shape
  Collections
Sara Hahner Souhaib Attaiki Jochen Garcke Maks Ovsjanikov
We introduce a novel learning-based method for encoding and manipulating 3D
surface meshes. Our method is specifically designed to create an interpretable
embedding space for deformable shape collections. Unlike previous 3D mesh
autoencoders that require meshes to be in a 1-to-1 correspondence, our approach
is trained on diverse meshes in an unsupervised manner. Central to our method
is a spectral pooling technique that establishes a universal latent space,
breaking free from traditional constraints of mesh connectivity and shape
categories. The entire process consists of two stages. In the first stage, we
employ the functional map paradigm to extract point-to-point (p2p) maps between
a collection of shapes in an unsupervised manner. These p2p maps are then
utilized to construct a common latent space, which ensures straightforward
interpretation and independence from mesh connectivity and shape category.
Through extensive experiments, we demonstrate that our method achieves
excellent reconstructions and produces more realistic and smoother
interpolations than baseline approaches.