GeoDeep4Mesh: Geometric Deep Learning on the Mesh Manifold
3 Février 2022
Catégorie : Doctorant
The core of this proposed project is on developing mathematically principled generative frameworks for deep learning on non-Euclidean domains such as graphs and manifolds. This project will touch upon some of the most challenging problems in different fields such as computer vision and graphics, where generative models are crucial. The research topic itself is very timely in terms of need and applicability of the systems targeted. This research also seeks to advance fundamental tools, that are not only of high relevance in terms of intellectual merit but also in broad impact.
Geometric deep learning; generative models in non-Euclidean domains; face and body analysis
While deep learning methods have been studied for quite a long time on regular domains like for images, learning solutions capable of working on non-Euclidean data like graphs, meshes or point clouds have been defined more recently. This area of research is attracting more and more interest for the potential applications in many disparate domains, going from modeling of social networks, molecules matching and design, to surface classification and recognition. This has originated a new area of investigation broadly known as Geometric Deep Learning. In this thesis proposal, we aim at investigating new solutions for learning on 2D manifolds. On the one hand, our idea is to attack the problem by proposing equivariant networks capable of accounting for 3D transformations as well as embedding new metrics in the loss that are computed on the surface. On the other hand, we will propose generative models for meshes and point clouds that also account for the temporal dimension.