03. – 08. März 2019, Dagstuhl-Seminar 19102

3D Morphable Models


Bernhard Egger (MIT – Cambridge, US)
William Smith (University of York, GB)
Christian Theobalt (MPI für Informatik – Saarbrücken, DE)
Thomas Vetter (Universität Basel, CH)

Auskunft zu diesem Dagstuhl-Seminar erteilt

Dagstuhl Service Team


Gemeinsame Dokumente
Programm des Dagstuhl-Seminars [pdf]


After 20 years of research around 3D Morphable Models (3DMM), this Dagstuhl Seminar aims to capture the key ideas and persons in the field and bring the community interested in 3DMMs of faces and bodies together for the first time. Not only will this recognise the 20th birthday of the original paper but, to a greater extent, facilitate a collaborative approach to current limitations and open research questions and address the lack of comparability of different approaches and coordination within the community.

A 3DMM is a statistical object model separating shape from appearance variation. Typically, 3DMMs are used as a statistical prior in computer graphics and vision. A model is learned from high quality 3D scans of multiple object instances. It reduces the dimensionality and provides a low-dimensional, parametric object representation. The resulting model is generative, which means that from a set of randomly sampled parameters a novel realistic object instance arises. The original model was defined on human faces and combines a 3D shape and appearance model. It was applied for 3D reconstruction of faces from still 2D images and 3D manipulation of those 2D images.

Today, 3DMMs of the human body and faces are well researched and adopted by industry. Besides various applications in computer graphics and vision, such models are highly suitable for use in medical imaging, surgical planning, psychology, ergonomics, and anthropology and are even proven to be applicable in modeling human cognition. Some of the application areas led to commercial products in industry. These products range from the entertainment industry through the fashion business to security applications incorporating face recognition.

Beyond current applications, emerging research directions make 3DMMs once again very timely. Recently, 3DMMs have been rediscovered in the context of deep learning. They provide a generative appearance model for self-supervision and parameterise nonrigid correspondence in geometric deep learning. Progress in 3D shape analysis and shape spaces provides new perspectives on 3DMMs via models of shape collections and shape differences.

The main topics we will cover in this Dagstuhl Seminar are correspondence, optimization, realism, physics, and evaluation in context of 3DMMs. The goals of the seminar are:

  • to build a diverse, multidisciplinary, and widely spread community to identify and approach future challenges
  • to come up with a set of open measurable challenges
  • to provide a perfect breeding ground for future collaboration
  • to initiate an edited book or a survey paper with broad support

Motivation text license
  Creative Commons BY 3.0 DE
  Bernhard Egger, William Smith, Christian Theobalt, and Thomas Vetter


  • Computer Graphics / Computer Vision


  • 3D Computer Vision
  • Computer Graphics
  • Statistical Modelling
  • Analysis-by-Synthesis
  • Generative Models


In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.


Download Übersichtsflyer (PDF).


Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von
Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.