Noam Aigerman (pronounced "Noh-uhm", like Noah with an M at the end.)
Assistant Professor, University of Montreal
Associate Academic Member, Mila


I am a computer scientist working on problems related to machine learning and 3D geometry. My research lies at the intersection of geometry processing, deep learning, and optimization, with applications in 3D vision and computer graphics. Currently, I mostly focus on using geometry processing to devise thoeretically-grounded machine learning approaches for 3D problems; and, vice-versa, approaching geometry processing tasks from a machine learning perspective.
Yes, this is I!

Group

Tianshu Kuai (PhD 2024 - )
Darsh Kaushik (MSc 2024 - )
Martin El Mqirmi (MSc 2024 - )

Teaching

IFT6095 - Neural Geometry Processing (winter 2025)
IFT6759 - Advanced Machine Learning Projects (winter 2025)
IFT6095 - Neural Geometry Processing (winter 2024)


Publications   (hover over a project's image for a one-sentence summary)

A chosen region on the back of the armadillo is deformed to different types of wings, based on different input text prompts.
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda, Vladimir Kim, Noam Aigerman, Amit Haim Bermano, Thibault Groueix
ACM SIGGRAPH Asia 2024
Paper | Project Page
A coarse shape is added local details based on the details of five chosen fine shapes.
DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
Qimin Chen, Zhiqin Chen, Vladimir G. Kim, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
ECCV 2024
Paper | Project Page
Two different models, animated by our method
Temporal Residual Jacobians for Rig-free Motion Transfer
Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra
ECCV 2024
Paper | Project Page
Four tilings of the plane automatically generated by our method, for four different symmetry patterns containing rotations and reflections, and four different prompts ('Flamenco dancer', 'Ballet dancer', 'Alligator', 'Unicorn').
Generative Escher Meshes
Noam Aigerman, Thibault Groueix
ACM SIGGRAPH 2024
Paper | Code
An extreme elastic deformation of a NeRF, without any visual artifacts as the deformation is guaranteed to be 1-to-1.
TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations
Bo Sun, Thibault Groueix, Chen Song, Qixing Huang, Noam Aigerman
CVPR 2024
Paper | Code
A 1-to-1 map between two 3D models which respects semantic correspondence, computed automatically using our method. Map is visualized by placing a checker texture on the left mesh and using the map to transfer the texture to the right mesh.
Neural Semantic Surface Maps
Luca Morreale, Noam Aigerman, Vladimir G. Kim, Niloy Mitra
Computer Graphics Forum (Eurographics 2024)
Project page
Top: point clouds are generated through a pretrained generative model, and are used to deform and swap meshes (bottom).
Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models
Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie
ACM SIGGRAPH Asia 2023
Project page
The cow is deformed into two desired shapes described by a text prompt, in this case a turtle and a stag.
TextDeformer: Geometry Manipulation using Text Guidance
William Gao, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, Rana Hanocka
ACM SIGGRAPH 2023
Project page
Face meshes from various datasets, with different triangulations, can be automatically deformed by our network into two facial expressions.
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
Dafei Qin, Jun Saito, Noam Aigerman, Thibault Groueix, Taku Komura
ACM SIGGRAPH 2023
Paper
A coarse mesh is progressively refined using a neural network, given additional details transmitted.
Neural Progressive Meshes
Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman, Alec Jacobson
ACM SIGGRAPH 2023
Paper
A point is selected on the mesh by a user, and a region containing this point is selected by our network, such that the region can UV-mapped with low-distortion (UV map visualized with a square texture on the selected region).
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka
CVPR 2023
Paper | Project page
Top: our previous method can produce a 1-to-1 UV map of the bunny into the cross, but has distortion and scales some parts of mesh (in red). Bottom: this paper can also compute a 1-to-1 UV map, but also ensure it has low distortion and less scaling.
Isometric Energies for Recovering Injectivity in Constrained Mapping
Xingyi Du, Danny M. Kaufman, Qingnan Zhou, Shahar Z. Kovalsky, Yajie Yan, Noam Aigerman, Tao Ju
ACM SIGGRAPH Asia 2022
Project page
A chair with parts of it missing (left) is completed by our method to a full chair (middle) by copying and transforming existing parts to missing areas, while competing generative approaches produce fuzzy approximiations (right).
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Bo Sun, Vladimir G. Kim, Noam Aigerman, Qixing Huang, Siddhartha Chaudhuri
ECCV 2022
Paper | Code
Our method produces an atlas with a complex topology for a set of shapes.
Learning Joint Surface Atlases
Theo Deprelle, Thibault Groueix, Noam Aigerman, Vladimir G. Kim, Mathieu Aubry
ECCV Workshop on Learning to Generate 3D Shapes and Scenes, 2022
Paper | Code
left: a UV map predicted by the network, almost identical to the ground-truth. Right: the network correctly reposes the bunny to the poses demonstrated by the human.
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, Thibault Groueix
ACM SIGGRAPH 2022 (journal track)
Paper | Code
A diagram of our spherical convolution operator.
Mobius Convolutions for Spherical CNNs
Thomas Mitchel, Noam Aigerman, Vladimir G. Kim, Michael Kazhdan
ACM SIGGRAPH 2022
Paper | Code
The framework learns to represent a surface map from a coarse atlasnet-like MLP composed with a CNN that adds details
Neural Convolutional Surfaces
Luca Morreale, Noam Aigerman, Paul Guerrero, Vladimir G. Kim, Niloy Mitra
CVPR 2022
Project Page
Various poses (orange) generated by our method from a few landmark poses (gray).
GLASS: Geometric Latent Augmentation for Shape Spaces
Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir G. Kim, Matthew Fisher, Niloy Mitra
CVPR 2022
Paper
A mesh (right) generated by optimizing the alignment of its edges to the input vector field (left).
Differentiable Surface Triangulation
Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov, Paul Guerrero
ACM SIGGRAPH ASIA 2021
Paper | Code
A mesh (left) is paramterized to an initial parameterization (middle) with local inversions of triangles and global overlaps of the boundary, which are then alleviated through our optimization (right).
Optimizing Global Injectivity for Constrained Parameterization
Xingyi Du, Danny M. Kaufman, Qingnan Zhou, Shahar Z. Kovalsky, Yajie Yan, Noam Aigerman, Tao Ju
ACM SIGGRAPH ASIA 2021
Project Page
A sequence of reconstructed surfaces using our algorithm, exhibiting good consistent correspondences between each frame in the sequence them (visualized via texture that exhibits the correspondence).
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri, Shaifali Parashar, Mathieu Salzmann, Pascal Fua, Noam Aigerman
ICCV 2021
Paper | Video
A 2D brush is swept along a spiraling trajectory (left), tracing the golden horn (right).
Swept Volumes via Spacetime Numerical Continuation
Silvia Sellán, Noam Aigerman, Alec Jacobson
ACM SIGGRAPH 2021
Project Page
Coarse voxel grids (red) are refined into different types of plants (yellow), based on the input desired style (green).
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri
CVPR 2021 (oral)
Paper | Code
Two surfaces are repsented as 2D-to-3D maps via two overfitted neural networks. Since they are both differentiable,this in turn enables optimizing a surface-to-surface map (via h) in a completely differentiable manner.
Neural Surface Maps
Luca Morreale, Noam Aigerman, Vladimir G. Kim, Niloy J. Mitra
CVPR 2021
Project Page
A point cloud is meshed using our neural network.
Learning Delaunay Surface Elements for Mesh Reconstruction
Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy J. Mitra, Maks Ovsjanikov
CVPR 2021 (oral)
Project Page
The framework of our method.
Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri, Leonidas Guibas
CVPR 2021
Project Page
Our method approximates the input heightfield surface (left) by a piecewise-developable heightfield surface (right).
Developability of Heightfields via Rank Minimization
Silvia Sellán, Noam Aigerman, Alec Jacobson
ACM SIGGRAPH 2020
Project Page
A coarse mesh is subidivided via a neural network, which restores natural geometric features without over-smoothing.
Neural Subdivision
Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri, Noam Aigerman, Alec Jacobson
ACM SIGGRAPH 2020
Paper | Code
Mapping a mesh into a non-convex domain without any inversions, yielding a globally injective map
Lifting Simplices to Find Injectivity
Xingyi Du, Noam Aigerman, Qingnan Zhou, Shahar Kovalsky, Yajie Yan, Danny M. Kaufman, Tao Ju
ACM SIGGRAPH 2020
Paper | Project Page | Code | Data Set | Video
Deforming humanoids to match example poses via a neural network, while preserving details.
Neural Cages for Detail-Preserving 3D Deformations
Wang Yifan, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, Olga Sorkine-Hornung
CVPR 2020 (oral)
Paper | Project Page
Comparison of reconstruction quality of the hybrid reconstruction versus the two components of the hybrid.
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
Omid Poursaeed, Matthew Fisher, Noam Aigerman, Vladimir G. Kim
ECCV 2020
Project Page
An embedding of a mesh into a spherical orbifold, which can tile the sphere.
Spherical Orbifold Tutte Embeddings
Noam Aigerman, Shahar Kovalsky, Yaron Lipman
ACM SIGGRAPH 2017
Paper | Low Res | Code
A convolution of a filter on a spherical surface is well-defined on the surface's toric 4-cover.
Convolutional Neural Networks on Surfaces via Seamless Toric Covers
Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim, Yaron Lipman
ACM SIGGRAPH 2017
Paper | Low Res | Code
An embedding of a mesh into a hyperbolic orbifold, which can tile the Poincare-disk model of the hyperbolic plane.
Hyperbolic Orbifold Tutte Embeddings
Noam Aigerman, Yaron Lipman
ACM SIGGRAPH Asia 2016
Paper | Low Res | Code
An embedding of a mesh into a planar orbifold, which can also be used to generate seamless quads on the mesh.
Orbifold Tutte Embeddings
Noam Aigerman, Yaron Lipman
ACM SIGGRAPH Asia 2015
Paper | Low Res | Code
A large-scale bijective parametrization of a tetrahedral mesh to a ball.
Large Scale Bounded Distortion Mappings
Shahar Kovalsky, Noam Aigerman, Ronen Basri, Yaron Lipman
ACM SIGGRAPH Asia 2015
Paper | Low Res | Project Page
Two identical bijective maps between two surface-meshes produced for two different cut placements.
Seamless Surface Mappings
Noam Aigerman, Roi Poranne, Yaron Lipman
ACM SIGGRAPH 2015
Paper | Low Res
A low distortion bijective map between two surface-meshes.
Lifted Bijections for Low Distortion Surface Mappings
Noam Aigerman, Roi Poranne, Yaron Lipman
ACM SIGGRAPH 2014
Paper | Low Res
The 'most conformal' mapping of a volumetric cube, subject to repositioning its eight corners.
Controlling Singular Values with Semidefinite Programming
Shahar Kovalsky, Noam Aigerman, Ronen Basri, Yaron Lipman
ACM SIGGRAPH 2014
Paper | Low Res | Project Page
A bounded-distortion, globally bijective map, mapping a tetrahedral mesh to a polycube.
Injective and Bounded Distortion Mappings in 3D
Noam Aigerman, Yaron Lipman
ACM SIGGRAPH 2013
Paper | Low Res | Project Page (code + data)
Others
A deformation of a bar.
Computational Aspects of Mappings
Tutorial given at the IGS 2016 summer school (with Shahar Kovalsky)
Slides (pdf)
Research, in a nutshell