We introduce SignNet and BasisNet--new neural architectures that are invariant to two key
symmetries displayed by eigenvectors:(i) sign flips, since if $ v $ is an eigenvector then so is …

Obtaining personalized 3D animatable avatars from a monocular camera has several real
world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very …

Abstract We propose Text2Scene, a method to automatically create realistic textures for
virtual scenes composed of multiple objects. Guided by a reference image and text …

We consider the problem of learning implicit neural representations (INRs) for signals on
non-Euclidean domains. In the Euclidean case, INRs are trained on a discrete sampling of a …

We address the problem of aligning real-world 3D data of garments, which benefits many
applications such as texture learning, physical parameter estimation, generative modeling of …

Parametric feature grid encodings have gained significant attention as an encoding
approach for neural fields since they allow for much smaller MLPs, which significantly …

Physics-informed neural networks (PINNs) have demonstrated promise in solving forward
and inverse problems involving partial differential equations. Despite recent progress on …

Geometric frameworks for analyzing curves are common in applications as they focus on
invariant features and provide visually satisfying solutions to standard problems such as …

In this paper we tackle the problem of generating conformers of a molecule in 3D space
given its molecular graph. We parameterize these conformers as continuous functions that …

Learning functions defined on non‐flat domains, such as outer surfaces of non‐rigid shapes,
is a central task in computer vision and geometry processing. Recent studies have explored …