Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Important new developments have appeared since the most recent direct survey on shape
correspondence published almost a decade ago. Our survey covers the period from 2011 …

In recent years, the Transformer architecture has proven to be very successful in sequence
processing, but its application to other data structures, such as graphs, has remained limited …

Constrained by the limitations of learning toolkits engineered for other applications, such as
those in image processing, many mesh-based learning algorithms employ data flows that …

In this paper, we advocate the adoption of metric preservation as a powerful prior for
learning latent representations of deformable 3D shapes. Key to our construction is the …

We introduce a novel learning-based method to recover shapes from their Laplacian
spectra, based on establishing and exploring connections in a learned latent space. The …

The Transformer-based graph neural network models have achieved remarkable results in
graph representation learning in recent years. One of the main challenges in graph …

Geometric operators are common objects in surface-based shape analysis and geometry
processing. While the intrinsic Laplace–Beltrami operator has been a ubiquitous choice …

We consider the problem of localizing relevant subsets of non-rigid geometric shapes given
only a partial 3D query as the input. Such problems arise in several challenging tasks in 3D …

We consider the problem of human deformation transfer, where the goal is to retarget poses
between different characters. Traditional methods that tackle this problem assume a human …