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Data-Driven Physics for Human Soft Tissue Animation




Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

Author(s): Meekyoung Kim and Gerard Pons-Moll and Sergi Pujades and Sungbae Bang and Jinwwok Kim and Michael Black and Sung-Hee Lee
Journal: ACM Transactions on Graphics, (Proc. SIGGRAPH)
Volume: 36
Number (issue): 4
Year: 2017

Department(s): Perceiving Systems
Research Project(s): Physics of Body Shape and Motion
Bibtex Type: Article (article)
Paper Type: Journal

URL: http://dx.doi.org/10.1145/3072959.3073685

Links: video
Attachments: paper


  title = {Data-Driven Physics for Human Soft Tissue Animation},
  author = {Kim, Meekyoung and Pons-Moll, Gerard and Pujades, Sergi and Bang, Sungbae and Kim, Jinwwok and Black, Michael and Lee, Sung-Hee},
  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH)},
  volume = {36},
  number = {4},
  year = {2017},
  url = {http://dx.doi.org/10.1145/3072959.3073685}