Face Transfer with Multilinear Models

DSpace/Manakin Repository

Face Transfer with Multilinear Models

Citable link to this page

. . . . . .

Title: Face Transfer with Multilinear Models
Author: Vlasic, Daniel; Brand, Matthew; Pfister, Hanspeter; Popovic, Jovan

Note: Order does not necessarily reflect citation order of authors.

Citation: Vlasic, Daniel, Matthew Brand, Hanspeter Pfister, and Jovan Popovic. 2006. Face transfer with multilinear models. In Proceedings of the International Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH 2006 Courses: July 30-August 3, 2006, Boston, Massachusetts, ed. J. Dorsey, 426-433. New York, N.Y.: ACM Press.
Full Text & Related Files:
Abstract: Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities x expressions x visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.
Published Version: doi:10.1145/1185657.1185864
Other Sources: http://gvi.seas.harvard.edu/sites/all/files/SIG2005-FACES_0.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4238955

Show full Dublin Core record

This item appears in the following Collection(s)

  • FAS Scholarly Articles [6463]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

Search DASH

Advanced Search