no code implementations • 11 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.
no code implementations • 25 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.
no code implementations • 9 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.
no code implementations • 10 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.
1 code implementation • 15 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.
1 code implementation • 26 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.
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.
1 code implementation • 24 Jul 2022 • Bo Sun, Vladimir G. Kim, Noam Aigerman, QiXing Huang, Siddhartha Chaudhuri
Our key insight is to copy and deform patches from the partial input to complete missing regions.
no code implementations • 13 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.
1 code implementation • 5 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.
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.
1 code implementation • 28 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.
1 code implementation • 28 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.
1 code implementation • 12 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.
Ranked #1 on Surface Reconstruction on ANIM
1 code implementation • 22 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.
1 code implementation • CVPR 2022 • Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra
We investigate the problem of training generative models on a very sparse collection of 3D models.
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.
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.
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.
1 code implementation • CVPR 2021 • Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
During testing, a style code is fed into the generator to condition the refinement.
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.
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.
2 code implementations • 4 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.
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.