We present an automated technique for computing a map between two genus-...
The recent proliferation of 3D content that can be consumed on hand-held...
We propose an end-to-end deep-learning approach for automatic rigging an...
We present a technique for automatically producing a deformation of an i...
We present a neural technique for learning to select a local sub-region
...
This paper introduces a data-driven shape completion approach that focus...
This paper describes new techniques for learning atlas-like representati...
This paper introduces a framework designed to accurately predict piecewi...
This work is concerned with a representation of shapes that disentangles...
Möbius transformations play an important role in both geometry and
spher...
Finding multiple solutions of non-convex optimization problems is a
ubiq...
We propose a method for unsupervised reconstruction of a
temporally-cons...
Triangle meshes remain the most popular data representation for surface
...
We investigate the problem of training generative models on a very spars...
We propose a method for the unsupervised reconstruction of a
temporally-...
Maps are arguably one of the most fundamental concepts used to define an...
We propose a novel technique for producing high-quality 3D models that m...
We present a method for reconstructing triangle meshes from point clouds...
We propose a novel neural architecture for representing 3D surfaces, whi...
This paper introduces Neural Subdivision, a novel framework for data-dri...
We formulate a novel characterization of a family of invertible maps bet...
We propose a novel learnable representation for detail-preserving shape
...