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Generative Models for 3D Vision (Dagstuhl Seminar 25202)

Authors Laura Neschen, Bernhard Egger, Adam Kortylewski, William Smith, Stefanie Wuhrer and all authors of the abstracts in this report

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DagRep.15.5.96.pdf
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  • 18 pages

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Subject Classification

ACM Subject Classification
  • Computing methodologies → 3D imaging
  • Computing methodologies → Computer vision
  • Computing methodologies → Computer graphics
Keywords
  • 3D Computer Vision
  • Computer Graphics
  • Generative Models
  • Implicit Representations
  • Neural Rendering
  • Statistical Modelling

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Abstract

Generative models that allow synthesis of realistic 3D models have been of interest in computer vision and graphics for over 2 decades. While traditional methods use morphable models for this task, more recent works have adopted powerful tools from the 2D image domain such as generative adversarial networks, neural fields and diffusion models, and have achieved impressive results. The question of which tools are most suitable for applications such as reconstructing 3D geometry from partial data, and creating digital 3D content remains open. This report documents the program and outcomes of Dagstuhl Seminar 25202 titled "Generative Models for 3D Vision". This meeting of 25 researchers covered a variety of topics such as generative models and priors for 2D tasks, medical applications, and digital representations of humans, including how to evaluate and benchmark different methods. We summarise the discussions, presentations, and results of this seminar.

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Laura Neschen, Bernhard Egger, Adam Kortylewski, William Smith, and Stefanie Wuhrer. Generative Models for 3D Vision (Dagstuhl Seminar 25202). In Dagstuhl Reports, Volume 15, Issue 5, pp. 96-113, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/DagRep.15.5.96

Author Details

Laura Neschen
  • INRIA Rhône-Alpes, FR
Bernhard Egger
  • Friedrich-Alexander-Universität Erlangen-Nürnberg, DE
Adam Kortylewski
  • Universität Freiburg, DE and MPI für Informatik - Saarbrücken, DE
William Smith
  • University of York, GB
Stefanie Wuhrer
  • INRIA - Grenoble, FR
and all authors of the abstracts in this report
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