In this work, we are interested in generalizing convolutional neural networks (CNNs) from
low-dimensional regular grids, where image, video and speech are represented, to high …

Deep Learning's recent successes have mostly relied on Convolutional Networks, which
exploit fundamental statistical properties of images, sounds and video data: the local …

We introduce SDM-NET, a deep generative neural network which produces structured
deformable meshes. Specifically, the network is trained to generate a spatial arrangement of …

Surfaces serve as a natural parametrization to 3D shapes. Learning surfaces using
convolutional neural networks (CNNs) is a challenging task. Current paradigms to tackle this …

Abstract 3D geometric contents are becoming increasingly popular. In this paper, we study
the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

A distance metric known as non‐Euclidean distance deviates from the laws of Euclidean
geometry, which is the geometry that governs most physical spaces. It is utilized when …

The recent introduction of 3D shape analysis frameworks able to quantify the deformation of
a shape into another in terms of the variation of real functions yields a new interpretation of …

In this work, we present a novel method for capturing human body shape from a single
scaled silhouette. We combine deep correlated features capturing different 2D views, and …

Polygonal meshes have become the standard for discretely approximating 3D shapes,
thanks to their efficiency and high flexibility in capturing non-uniform shapes. This non …