AMI « Apport du Numérique au domaine du recyclage »
Le PEPR « Recyclage » lancé au printemps 2023 est structuré en : 5 axes verticaux « matériaux » (plastiques,...
18 Octobre 2024
Catégorie : Stagiaire
Geometric deep learning has emerged in the fields of computer graphics and computer vision, enabling deep learning models to operate on geometric data such as graphs, meshes, manifolds, and point clouds. Some notable models in this area include Graph Convolutional Networks (GCNs), PointNet, Geodesic Neural Networks (GNNs), and specialized architectures for 3D meshes, such as MeshNet and MeshCNN.
Motivated by these recent successes, we will explore and develop geometric deep learning models for a 3D mesh dataset. In particular, we are interested in 3D garment mesh data representing garment shapes in motion. Our specific focus will be on generative models capable of performing various downstream tasks, such as sequence inpainting and conditional generation.
We will proceed with the following tasks: