DagRep.12.3.97.pdf
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3D Morphable Models and Beyond (Dagstuhl Seminar 22121)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 12, Issue 3
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-11-14
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James Gardner, Bernhard Egger, William Smith, Christian Theobalt, and Stefanie Wuhrer. 3D Morphable Models and Beyond (Dagstuhl Seminar 22121). In Dagstuhl Reports, Volume 12, Issue 3, pp. 97-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.12.3.97
https://doi.org/10.4230/DagRep.12.3.97
Abstract
3D Morphable Models are models separating shape from appearance variation. Typically, they are used as a statistical prior in computer graphics and vision. Recent success with neural representations have caused a resurgence of interest in visual computing problems, leading to more accurate, higher fidelity, more expressive, and memory-efficient solutions. This report documents the program and the outcomes of Dagstuhl Seminar 22121, "3D Morphable Models and Beyond". This meeting of 39 researchers covered various topics, including 3D morphable models, implicit neural representations, physics-inspired approaches, and more. We summarise the discussions, presentations and results of this workshop.
Subject Classification
ACM Subject Classification
- Computing methodologies → Computer graphics
- Computing methodologies → Image-based rendering
- Computing methodologies → Shape modeling
- Computing methodologies → Animation
- Computing methodologies → Computer vision
- Computing methodologies → 3D imaging
Keywords
- 3D Computer Vision
- Generative Models
- Neural Rendering
- Implicit Representations
- Computer Graphics
- Statistical Modelling
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