EMU is an efficient and scalable model to simulate bulk musculoskeletal motion with heterogenous materials. First, EMU requires no model reductions, or geometric coarsening, thereby producing results visually accurate when compared to an FEM simulation. Second, EMU is efficient and scales much better than state-of-the-art FEM with the number of elements in the mesh, and is more easily parallelizable. Third, EMU can handle heterogeneously stiff meshes with an arbitrary constitutive model, thus allowing it to simulate soft muscles, stiff tendons and even stiffer bones all within one unified system. These three key characteristics of EMU enable us to efficiently orchestrate muscle activated skeletal movements. We demonstrate the efficacy of our approach via a number of examples with tendons, muscles, bones and joints.
@article{Modi_Fulton_Jacobson_Sueda_Levin_2020,
title={EMU: Efficient Muscle Simulation in Deformation Space},
url={http://dx.doi.org/10.1111/cgf.14185}, DOI={10.1111/cgf.14185},
journal={Computer Graphics Forum}, publisher={Wiley},
author={Modi, V. and Fulton, L. and Jacobson, A. and Sueda, S. and Levin, D.I.W.},
year={2020},
month={Dec}
}
This work is funded in part by National Science Foundation (CAREER-1846368), NSERC Discovery (RGPIN-2017-05524, RGPIN-2017-05235, RGPAS-2017-507938), Connaught Fund (503114), CFI-JELF Fund, Accelerator (RGPAS-2017-507909), New Frontiers of Research Fund (NFRFE–201), the Ontario Early Research Award program, the Canada Research Chairs Program, the Fields Centre for Quantitative Analysis and Modelling and gifts by Adobe Systems, Autodesk and MESH Inc. We thank John Kanji, and Josh Holinaty for help with designing figures; Rinat Abdrashitov for his video editing skills; Josh Holinaty for lending us his beautiful voice in the video; Sarah Kushner, Honglin Chen, Abhishek Madan, Hsueh-Ti Derek Liu and Darren Moore for proofreading; John Hancock for IT support; anonymous reviewers for their helpful comments and suggestions.