Muscle and Fascia Simulation with Extended Position Based Dynamics

Abstract

Recent research on muscle and fascia simulation for visual effects relies on numerical methods such as the finite element method or finite volume method. These approaches produce realistic results, but require high computational time and are complex to set up. On the other hand, position‐based dynamics offers a fast and controllable solution to simulate surfaces and volumes, but there is no literature on how to implement constraints that could be used to realistically simulate muscles and fascia for digital creatures with this method. In this paper, we extend the current state‐of‐the‐art in Position‐Based Dynamics to efficiently compute realistic skeletal muscle and superficial fascia simulation. In particular, we embed muscle fibres in the solver by adding an anisotropic component to the distance constraints between mesh points and apply overpressure to realistically model muscle volume changes under contraction. In addition, we also define a modified distance constraint for the fascia that allows compression and enables the user to scale the constraint's original distance to gain elastic potential at rest. Finally, we propose a modification of the extended position‐based dynamics algorithm to properly compute different sets of constraints and describe other details for proper simulation of character's muscle and fascia dynamics.

Data‐Driven Facial Simulation

“…For the simulation results presented in the following subsections we used XPBD parameters fit for muscles (damping 1.0; stiffness [RMSQ19]), with five XPBD iterations and three activation smoothing iterations producing production‐quality results at very good speeds (between 23 and 122 frames per second depending on the complexity of the character used).…”

“…To demonstrate the efficiency of the workflow we simulate the face geometry using a modified Extended Position–Based Dynamics ( XPBD ) algorithm [MMC16]. XPBD is a stable and fast method for solving physical simulations of surfaces and volumes that comprised different materials, including muscles [RMSQ19]. XPBD relies on an iterative solver based on the Gauss–Siedel method and uses compliant constraints which achieve excellent results in few iterations.…”

“…Simulation could be a possible solution to improve the quality of standard facial rigs, but while many different approaches were identified to increase the believability and realism of the body of digital characters with physical simulation (see [TBHF03, TSIF05, BTS*05, LGK*12, SZK15, MMT*16, JBE*16, RMSQ18, RMSQ19]), this is still an open issue for the face. The muscular structure of the face is very complex to represent digitally, with 43 muscles that interact closely together in a very limited space, making the dynamic rig harder to set up and the simulation more difficult to solve without artefacts.…”

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

“…Also, they are able to calculate more visually plausible deformations than MS models. Hence, they have been used in a wide range of interactive graphical applications (Tian et al, 2013;Macklin et al, 2014), particularly for modeling the deformation of human body parts (Zhu et al, 2008;Sidorov and Marshall, 2014;Romeo et al, 2020), and robotic manipulation tasks (Caccamo et al, 2016;Guler et al, 2017). A disadvantage of PBD methods is that they simulate physical deformation less accurately than constitutive models, since they are geometrically motivated.…”

Viper

“…Highly relevant to our work is the simulation of skin layered over volumetric primitives pioneered by Li et al [2013], and its realization in commercial software [Vital Mechanics 2018], as well as other existing research [Saito and Yuen 2017], industry [Ziva Dynamics 2018], and proprietary solutions [Clutterbuck and Jacobs 2010] to this complementary problem. Recently, Romeo et al [2018] proposed the use of PBD for muscle simulation, but its ≈ 40s/frame of processing time makes it unsuitable to interactive applications.…”