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Modeling and Estimation of Nonlinear Skin Mechanics for Animated Avatars

Cristian Romero, Miguel A. Otaduy, Dan Casas, Jesus Perez

Hybrid SMPL-FEM avatar with custom nonlinear anisotropic material; skin thickness and mechanical properties optimized from 4D captures.

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Abstract

Data‐driven models of human avatars have shown very accurate representations of static poses with soft‐tissue deformations. However they are not yet capable of precisely representing very nonlinear deformations and highly dynamic effects. Nonlinear skin mechanics are essential for a realistic depiction of animated avatars interacting with the environment, but controlling physics‐only solutions often results in a very complex parameterization task. In this work, we propose a hybrid model in which the soft‐tissue deformation of animated avatars is built as a combination of a data‐driven statistical model, which kinematically drives the animation, an FEM mechanical simulation. Our key contribution is the definition of deformation mechanics in a reference pose space by inverse skinning of the statistical model. This way, we retain as much as possible of the accurate static data‐driven deformation and use a custom anisotropic nonlinear material to accurately represent skin dynamics. Model parameters including the heterogeneous distribution of skin thickness and material properties are automatically optimized from 4D captures of humans showing soft‐tissue deformations.

How to read this

Category: Method: a hybrid data-driven/FEM model of nonlinear skin mechanics for avatars
Contributions: A hybrid avatar combining a data-driven statistical model (kinematically driving the animation) with an FEM mechanical simulation for dynamics.
Defining deformation mechanics in a reference pose space via inverse skinning of the statistical model, retaining accurate static deformation while adding a custom anisotropic nonlinear material.
Automatic optimization of model parameters, including heterogeneous skin thickness and material properties, from 4D captures.
Context: Builds on data-driven soft-tissue animation such as Kim et al.'s 'Data-Driven Physics for Human Soft Tissue Animation', layered onto an SMPL-style statistical body.Builds on: Data-Driven Physics for Human Soft Tissue Animation
Correctness: Parameters are fit from 4D captures of humans showing soft-tissue deformation, so accuracy is tied to that capture data and the chosen material model; the hybrid design aims to keep static accuracy while improving dynamics, but generalization beyond captured subjects/motions is the caveat.
Clarity: Technical; a first pass conveys the statistical-plus-FEM split, a second pass is needed for the reference-space mechanics and parameter estimation.
How to read it: Read first for how kinematic statistical deformation and FEM dynamics are combined in reference pose space; a second pass on the inverse skinning and material/parameter estimation pays off for implementation.

Builds on

Data-Driven Physics for Human Soft Tissue Animation 2017

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Related work

Data-driven Modeling of Skin and Muscle Deformation 2008 / SIGGRAPH
How to Build a Human: Practical Physics-Based Character Animation 2016 / DigiPro
Data-Driven Physics for Human Soft Tissue Animation 2017 / SIGGRAPH
Building Accurate Physics-based Face Models from Data 2019 / SCA

Keywords

nonlinear skin mechanics soft-tissue deformation finite element method fem simulation statistical body model inverse skinning anisotropic material constitutive material hybrid model data-driven reference pose space 4d capture parameter estimation numerical optimization animated avatars

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