Abstract
The availability of affordable and portable depth sensors has made scanning objects and people simpler than
ever. However, dealing with occlusions and missing parts
is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single
or multiple partial scans has received increasing attention
in recent years. In this work, we propose a novel learningbased method for the completion of partial shapes. Unlike
the majority of existing approaches, our method focuses on
objects that can undergo non-rigid deformations. The core
of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the
representation in this latent space that best fits the generated shape to the known partial input. The completed shape
exhibits a realistic appearance on the unknown part. We
show promising results towards the completion of synthetic
and real scans of human body and face meshes exhibiting
different styles of articulation and partiality