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What and Who
Title:Capturing and Learning Digital Humans
Speaker:Gerard Pons-Moll
coming from:Max-Planck-Institut für Informatik - D2
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 2 May 2018
Duration:60 Minutes
Building:E1 5
The world is shifting towards a digitization of everything -- music, books, movies and news in digital form are common in our everyday lives. Digitizing human beings would redefine the way we think and communicate (with other humans and with machines), and it is necessary for many applications -- for example, to transport people into virtual and augmented reality, for entertainment and special effects in movies, and for medicine and psychology.

Currently, digital people models typically lack realism or require time-consuming manual editing of physical simulation parameters. Our hypothesis is that better and more realistic models of humans and clothing can be learned directly by capturing real people using 4D scans, images, and depth and inertial sensors. Combining statistical machine learning techniques and geometric optimization, we create realistic statistical models from the captured data.

To be able to digitize people from low-cost ubiquitous sensors (RGB cameras, depth or small number of wearable inertial sensors), we leverage the learned statistical models -- which are robust to noise and missing data.

Name(s):Jennifer Müller
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):
  • Jennifer Müller, 04/27/2018 01:55 PM
  • Anna Rossien, 02/05/2018 02:07 PM -- Created document.