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https://doi.org/10.1109/3dv.20...
Article . 2013 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2023
Data sources: DBLP
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Statistical Analysis of 3D Faces in Motion

Authors: Timo Bolkart; Stefanie Wuhrer;

Statistical Analysis of 3D Faces in Motion

Abstract

We perform statistical analysis of 3D facial shapes in motion over different subjects and different motion sequences. For this, we represent each motion sequence in a multilinear model space using one vector of coefficients for identity and one high-dimensional curve for the motion. We apply the resulting statistical model to two applications: to synthesize motion sequences, and to perform expression recognition. En route to building the model, we present a fully automatic approach to register 3D facial motion data, Based on a multilinear model, and show that the resulting registrations are of high quality.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Top 10%