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|a 10.1109/TVCG.2024.3372117
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|a eng
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1 |
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|a Morgenstern, Wieland
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Animatable Virtual Humans
|b Learning Pose-Dependent Human Representations in UV Space for Interactive Performance Synthesis
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|c 2024
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|a Text
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|a Date Completed 22.04.2024
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|a Date Revised 22.04.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a We propose a novel representation of virtual humans for highly realistic real-time animation and rendering in 3D applications. We learn pose dependent appearance and geometry from highly accurate dynamic mesh sequences obtained from state-of-the-art multiview-video reconstruction. Learning pose-dependent appearance and geometry from mesh sequences poses significant challenges, as it requires the network to learn the intricate shape and articulated motion of a human body. However, statistical body models like SMPL provide valuable a-priori knowledge which we leverage in order to constrain the dimension of the search space, enabling more efficient and targeted learning and to define pose-dependency. Instead of directly learning absolute pose-dependent geometry, we learn the difference between the observed geometry and the fitted SMPL model. This allows us to encode both pose-dependent appearance and geometry in the consistent UV space of the SMPL model. This approach not only ensures a high level of realism but also facilitates streamlined processing and rendering of virtual humans in real-time scenarios
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Bagdasarian, Milena T
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hilsmann, Anna
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Eisert, Peter
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 5 vom: 11. Apr., Seite 2644-2650
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|g volume:30
|g year:2024
|g number:5
|g day:11
|g month:04
|g pages:2644-2650
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|u http://dx.doi.org/10.1109/TVCG.2024.3372117
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