Search Results for author: Souhaib Attaiki

Found 11 papers, 8 papers with code

GANFusion: Feed-Forward Text-to-3D with Diffusion in GAN Space

no code implementations21 Dec 2024 Souhaib Attaiki, Paul Guerrero, Duygu Ceylan, Niloy J. Mitra, Maks Ovsjanikov

We observe that GAN- and diffusion-based generators have complementary qualities: GANs can be trained efficiently with 2D supervision to produce high-quality 3D objects but are hard to condition on text.

Denoising Text to 3D

Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction

1 code implementation NeurIPS 2023 Souhaib Attaiki, Maks Ovsjanikov

We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data.

Decoder

Unsupervised Representation Learning for Diverse Deformable Shape Collections

no code implementations27 Oct 2023 Sara Hahner, Souhaib Attaiki, Jochen Garcke, Maks Ovsjanikov

Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner.

Representation Learning

AtomSurf : Surface Representation for Learning on Protein Structures

2 code implementations28 Sep 2023 Vincent Mallet, Souhaib Attaiki, Yangyang Miao, Bruno Correia, Maks Ovsjanikov

In particular, there is a lack of direct and fair benchmark comparison between the best available surface-based learning methods against alternative representations such as graphs.

Atom3D benchmark Protein Structure Prediction

Understanding and Improving Features Learned in Deep Functional Maps

no code implementations CVPR 2023 Souhaib Attaiki, Maks Ovsjanikov

In this paper, we show that under some mild conditions, the features learned within deep functional map approaches can be used as point-wise descriptors and thus are directly comparable across different shapes, even without the necessity of solving for a functional map at test time.

Generalizable Local Feature Pre-training for Deformable Shape Analysis

1 code implementation CVPR 2023 Souhaib Attaiki, Lei LI, Maks Ovsjanikov

We observe that with proper training, learned features can be useful in such tasks, but, crucially, only with an appropriate choice of the receptive field size.

Transfer Learning

NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching

1 code implementation14 Jan 2023 Souhaib Attaiki, Maks Ovsjanikov

We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes.

Denoising

SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence

1 code implementation16 Sep 2022 Lei LI, Souhaib Attaiki, Maks Ovsjanikov

In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust non-rigid matching.

Contrastive Learning

Smoothness and effective regularizations in learned embeddings for shape matching

1 code implementation14 Dec 2021 Riccardo Marin, Souhaib Attaiki, Simone Melzi, Emanuele Rodolà, Maks Ovsjanikov

In this study, we analyze, for the first time, properties that arise in data-driven learned embedding and their relation to the shape-matching task.

Relation

DPFM: Deep Partial Functional Maps

1 code implementation19 Oct 2021 Souhaib Attaiki, Gautam Pai, Maks Ovsjanikov

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality.

DiffusionNet: Discretization Agnostic Learning on Surfaces

4 code implementations1 Dec 2020 Nicholas Sharp, Souhaib Attaiki, Keenan Crane, Maks Ovsjanikov

We introduce a new general-purpose approach to deep learning on 3D surfaces, based on the insight that a simple diffusion layer is highly effective for spatial communication.

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