Data-driven physics for human soft tissue animation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles ACM Transactions on Graphics Year : 2017

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.
Fichier principal
Vignette du fichier
meekyoungSIGGRAPH2017.pdf (5.4 Mo) Télécharger le fichier
Vignette du fichier
thumb_lg_VSMPL (1).png (40.91 Ko) Télécharger le fichier
thumb_lg_VSMPL.png (40.91 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Format : Figure, Image

Dates and versions

hal-02162187 , version 1 (21-06-2019)



Meekyoung Kim, Gerard Pons-Moll, Sergi Pujades, Seungbae Bang, Jinwook Kim, et al.. Data-driven physics for human soft tissue animation. ACM Transactions on Graphics, 2017, 36 (4), pp.1-12. ⟨10.1145/3072959.3073685⟩. ⟨hal-02162187⟩
89 View
443 Download



Gmail Facebook X LinkedIn More