Search Results for author: Noam Aigerman

Found 24 papers, 17 papers with code

Explorable Mesh Deformation Subspaces from Unstructured Generative Models

no code implementations11 Oct 2023 Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie

Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes.

Generative Escher Meshes

no code implementations25 Sep 2023 Noam Aigerman, Thibault Groueix

We thus consider both the mesh's tile-shape and its texture as optimizable parameters, rendering the textured mesh via a differentiable renderer.

Neural Semantic Surface Maps

no code implementations9 Sep 2023 Luca Morreale, Noam Aigerman, Vladimir G. Kim, Niloy J. Mitra

We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another.

Neural Progressive Meshes

no code implementations10 Aug 2023 Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman, Alec Jacobson

The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e. g., 3D meshes, over the Internet.


Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild

1 code implementation15 May 2023 Dafei Qin, Jun Saito, Noam Aigerman, Thibault Groueix, Taku Komura

We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild.

TextDeformer: Geometry Manipulation using Text Guidance

1 code implementation26 Apr 2023 William Gao, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, Rana Hanocka

Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.

DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization

1 code implementation CVPR 2023 Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka

We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.


Learning Joint Surface Atlases

no code implementations13 Jun 2022 Theo Deprelle, Thibault Groueix, Noam Aigerman, Vladimir G. Kim, Mathieu Aubry

We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes.

Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes

1 code implementation5 May 2022 Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, Thibault Groueix

This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.

Neural Convolutional Surfaces

no code implementations CVPR 2022 Luca Morreale, Noam Aigerman, Paul Guerrero, Vladimir G. Kim, Niloy J. Mitra

Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner.


Learning Proximal Operators to Discover Multiple Optima

1 code implementation28 Jan 2022 Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon

We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence.

object-detection Object Detection

Möbius Convolutions for Spherical CNNs

1 code implementation28 Jan 2022 Thomas W. Mitchel, Noam Aigerman, Vladimir G. Kim, Michael Kazhdan

M\"obius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equivalent of homographies.

Descriptive Image Segmentation +1

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

1 code implementation12 Nov 2021 Jan Bednarik, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua

The key to making these correspondences semantically meaningful is to guarantee that the metric tensors computed at corresponding points are as similar as possible.

Surface Reconstruction

Differentiable Surface Triangulation

1 code implementation22 Sep 2021 Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov, Paul Guerrero

Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

1 code implementation ICCV 2021 Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman

We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes.

Surface Reconstruction

Neural Surface Maps

1 code implementation CVPR 2021 Luca Morreale, Noam Aigerman, Vladimir Kim, Niloy J. Mitra

Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry.

Joint Learning of 3D Shape Retrieval and Deformation

1 code implementation CVPR 2021 Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri, Leonidas Guibas

In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.

3D Shape Retrieval Retrieval

Learning Delaunay Surface Elements for Mesh Reconstruction

1 code implementation CVPR 2021 Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov

We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.

Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling

no code implementations ECCV 2020 Omid Poursaeed, Matthew Fisher, Noam Aigerman, Vladimir G. Kim

We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i. e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i. e., a scalar function over the 3D volume, with its levels denoting surfaces.

Surface Reconstruction

Neural Subdivision

2 code implementations4 May 2020 Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri, Noam Aigerman, Alec Jacobson

During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch.

Neural Cages for Detail-Preserving 3D Deformations

1 code implementation CVPR 2020 Wang Yifan, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Olga Sorkine-Hornung

The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.

Cannot find the paper you are looking for? You can Submit a new open access paper.