Paper Info
Title
Data-driven physics for human soft tissue animation
Abstract
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.
Year
DOI
Venue
2017
10.1145/3072959.3073685
ACM Trans. Graph.
Keywords
Field
DocType
character animation,finite element method,statistical human shape,parameter estimation
Human-body model,Biological system,Computer graphics (images),Character animation,Artificial intelligence,Deformation (mechanics),Computer vision,Segmentation,Finite element method,Retargeting,Animation,Elasticity (economics),Physics
Journal
Volume
Issue
ISSN
36
4
0730-0301
Citations 
PageRank 
References 
19
0.56
50
Authors
7
Name
Order
Citations
PageRank
Meekyoung Kim1231.64
Gerard Pons-Moll280736.27
Sergi Pujades31006.23
Seungbae Bang4283.42
Jinwook Kim5211.28
Michael J. Black6112331536.41
Sung-Hee Lee733424.19