Portrait de Noam Aigerman

Noam Aigerman

Membre académique associé
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Vision par ordinateur
Site web
Google Scholar

Biographie

Je suis professeur adjoint à l'Université de Montréal. Précédemment, j’ai été chercheur scientifique chez Adobe. Je travaille sur des problèmes liés à la géométrie 3D et à l'apprentissage. Mes recherches se situent au carrefour du traitement de la géométrie, de l'infographie, de l'apprentissage profond et de l'optimisation.

Étudiants actuels

Martin El Mqirmi
Maîtrise recherche - UdeM
Github
Darsh Kaushik
Maîtrise recherche - UdeM
Github
Google Scholar
Tianshu Kuai
Doctorat - UdeM
tianshu.kuai@umontreal.ca
Site web
Github
Google Scholar
Thomas Peschlow
Maîtrise recherche - UdeM
Github

Publications

Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
Amir Barda
Matheus Gadelha
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without t… (voir plus)he need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.
2024-11-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
Amir Barda
Matheus Gadelha
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without t… (voir plus)he need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.
2024-11-30
ArXiv (prépublication)
doi.org
arxiv.org
Temporal Residual Jacobians For Rig-free Motion Transfer
Sanjeev Muralikrishnan
Niladri Shekhar Dutt
Siddhartha Chaudhuri
Noam Aigerman
Vladimir Kim
Matthew Fisher
Niloy J. Mitra
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume acces… (voir plus)s to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ .
2024-07-20
ArXiv (prépublication)
doi.org
arxiv.org
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial proper… (voir plus)ties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.
2024-03-04
ArXiv (prépublication)
doi.org
arxiv.org