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What and Who
Title:Dynamic and Groupwise Statistical Analysis of 3D Faces
Speaker:Timo Bolkart
coming from:Cluster of Excellence - Multimodal Computing and Interaction - MMCI
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Tuesday, 14 June 2016
Duration:-- Not specified --
Building:E1 4
This thesis proposes several methods to statistically analyze static and
dynamic 3D face data. First, we present a fully-automatic method to
robustly register entire facial motion sequences. The representation of
the 3D facial motion sequences obtained by the registration allows us to
perform statistical analysis of 3D face shapes in motion. We then
introduce a new localized multilinear model that is able to capture
fine-scale details while being robust to noise and partial occlusions.
To obtain a suitable registration for multilinearly distributed data, we
introduce a groupwise correspondence optimization method that jointly
optimizes a multilinear model and the registration of the 3D scans used
for training. To robustly learn a multilinear model from 3D face
databases with missing data, corrupt data, wrong semantic
correspondence, and inaccurate vertex correspondence, we propose a
robust model learning framework that jointly learns a multilinear model
and fixes the data. Finally, we present one application of our
registration methods, namely to obtain a sizing system that incorporates
the shape of an identity along with its motion. We introduce a general
framework to generate a sizing system for dynamic 3D motion data
Name(s):Ellen Fries
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
Ellen Fries/MPI-INF, 06/03/2016 09:26 AM
Last modified:
halma/MPII/DE, 01/17/2017 11:43 AM
  • Ellen Fries, 06/03/2016 09:28 AM -- Created document.