Chaudhuri, SiddharthaRitchie, DanielXu, KaiZhang, Hao (Richard)Jakob, Wenzel and Puppo, Enrico2019-05-052019-05-0520191017-4656https://doi.org/10.2312/egt.20191038https://diglib.eg.org:443/handle/10.2312/egt20191038Many important applications demand 3D content, yet 3D modeling is a notoriously difficult and inaccessible activity. This tutorial provides a crash course in one of the most promising approaches for democratizing 3D modeling: learning generative models of 3D structures. Such generative models typically describe a statistical distribution over a space of possible 3D shapes or 3D scenes, as well as a procedure for sampling new shapes or scenes from the distribution. To be useful by non-experts for design purposes, a generative model must represent 3D content at a high level of abstraction in which the user can express their goals-that is, it must be structure-aware. In this tutorial, we will take a deep dive into the most exciting methods for building generative models of both individual shapes as well as composite scenes, highlighting how standard data-driven methods need to be adapted, or new methods developed, to create models that are both generative and structure-aware. The tutorial assumes knowledge of the fundamentals of computer graphics, linear algebra, and probability, though a quick refresher of important algorithmic ideas from geometric analysis and machine learning is included. Attendees should come away from this tutorial with a broad understanding of the historical and current work in generative 3D modeling, as well as familiarity with the mathematical tools needed to start their own research or product development in this area.CCS ConceptsComputing methodologies> Probabilistic reasoningNeural networksShape analysis10.2312/egt.2019103847-51