Editing faces in videos

Amberg, Brian. Editing faces in videos. 2011, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: http://edoc.unibas.ch/diss/DissB_9701

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Editing faces in movies is of interest in the special effects industry. We aim at
producing effects such as the addition of accessories interacting correctly with
the face or replacing the face of a stuntman with the face of the main actor.
The system introduced in this thesis is based on a 3D generative face model.
Using a 3D model makes it possible to edit the face in the semantic space of pose,
expression, and identity instead of pixel space, and due to its 3D nature allows
a modelling of the light interaction. In our system we first reconstruct the 3D
face, which is deforming because of expressions and speech, the lighting, and
the camera in all frames of a monocular input video. The face is then edited by
substituting expressions or identities with those of another video sequence or by
adding virtual objects into the scene. The manipulated 3D scene is rendered back
into the original video, correctly simulating the interaction of the light with the
deformed face and virtual objects.
We describe all steps necessary to build and apply the system. This includes
registration of training faces to learn a generative face model, semi-automatic
annotation of the input video, fitting of the face model to the input video, editing
of the fit, and rendering of the resulting scene.
While describing the application we introduce a host of new methods, each
of which is of interest on its own. We start with a new method to register 3D
face scans to use as training data for the face model. For video preprocessing a
new interest point tracking and 2D Active Appearance Model fitting technique
is proposed. For robust fitting we introduce background modelling, model-based
stereo techniques, and a more accurate light model.
Advisors:Vetter, Thomas
Committee Members:Fitzgibbon, Andrew
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Ehemalige Einheiten Mathematik & Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Amberg, Brian and Vetter, Thomas
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:9701
Thesis status:Complete
Number of Pages:115 S.
Identification Number:
edoc DOI:
Last Modified:22 Jan 2018 15:51
Deposited On:30 Dec 2011 10:33

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