Animatable Virtual Humans: Learning pose-dependent human representations in UV space for interactive performance synthesis

• URL: http://arxiv.org/abs/2310.03615v1
• Date: Thu, 5 Oct 2023 15:49:44 GMT
• Title: Animatable Virtual Humans: Learning pose-dependent human representations in UV space for interactive performance synthesis
• Authors: Wieland Morgenstern, Milena T. Bagdasarian, Anna Hilsmann, Peter Eisert
• Abstract summary: We learn pose dependent appearance and geometry from highly accurate dynamic mesh sequences. We encode both pose-dependent appearance and geometry in the consistent UV space of the SMPL model.
• Score: 11.604386285817302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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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