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Thesis
Home   /   Thesis   /   GenPhi : 3D Generative AI conditioned by geometry, structure and physics

GenPhi : 3D Generative AI conditioned by geometry, structure and physics

Artificial intelligence & Data intelligence Computer science and software Engineering sciences Technological challenges

Abstract

The aim of this thesis is to design new 3D model generators based on Generative Artificial Intelligence (GenAI), capable of producing faithful, coherent and physically viable shapes. While 3D generation has become essential in many fields, current automatic generation approaches suffer from limitations in terms of respecting geometric, structural and physical constraints. The goal is to develop methods for integrating constraints related to geometry, topology, internal structure and physical laws, both stationary (equilibrium, statics) and dynamic (kinematics, deformation), right from the generation stage. The study will combine geometric perception, semantic enrichment and physical simulation approaches to produce robust, realistic 3D models that can be directly exploited without human intervention.

Laboratory

Département Intelligence Ambiante et Systèmes Interactifs (LIST)
Service Intelligence Artificielle pour le Langage et la Vision
Laboratoire Vision et Apprentissage pour l’analyse de scènes
Paris-Saclay
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