Physics-aided editing of simulation-ready muscles for visual effects

Turchet, Fabio; Romeo, Marco; Fryazinov, Oleg

doi:10.1145/2945078.2945158

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Facial physical muscle simulation using Finite Elements [CBF16, CLF17, CF19, SNF05] and Position‐Based Dynamics [Fra12, MMC16] is being researched as a means to reproduce such intrinsic non‐linearity of living tissues in digital characters. Although in some cases the results are undoubtedly good, improving over blendshape techniques, these techniques require a lot of time to model and configure the muscle rig or even edit the results of a muscle transfer process [CBE*15, TRF16]. Furthermore, physical simulation has to enrich the result of the blendshape animation rig, providing increased level of realism, but it is critical that the result still retains the expressivity and key facial features defined by artists.…”

Section: Related Workmentioning

confidence: 99%

Data‐Driven Facial Simulation

Romeo¹,

Schvartzman²

2020

Computer Graphics Forum

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In Visual Effects, the creation of realistic facial performances is still a challenge that the industry is trying to overcome. Blendshape deformation is used to reproduce the action of different groups of muscles, which produces realistic static results. However, this is not sufficient to generate believable and detailed facial performances of animated digital characters. To increase the realism of facial performances, it is possible to enhance standard facial rigs using physical simulation approaches. However, setting up a simulation rig and controlling material properties according to the performance is not an easy task and could take a lot of time and iterations to get it right. We present a workflow that allows us to generate an activation map for the fibres of a set of superficial patches we call pseudo‐muscles. The pseudo‐muscles are automatically identified using k‐means to cluster the data from the blendshape targets in the animation rig and compute the direction of their contraction (direction of the pseudo‐muscle fibres). We use an Extended Position–Based Dynamics solver to add physical simulation to the facial animation, controlling the behaviour of simulation through the activation map. We show the results achieved using the proposed solution on two digital humans and one fantastic cartoon character, demonstrating that the identified pseudo‐muscles approximate facial anatomy and the simulation properties are properly controlled, increasing the realism while preserving the work of animators.

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Section: Related Workmentioning

confidence: 99%

Data‐Driven Facial Simulation

Romeo¹,

Schvartzman²

2020

Computer Graphics Forum

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“…To overcome the difficult setup process required by FEM and FVM, companies and researchers have implemented specific tools [ziva, SSW*10], allowing artists to implement one muscle rig template and transfer it across similar creatures. Another solution is to provide artists with modelling tools designed to produce simulation‐ready geometries [TRF16]. Still, the process remains cumbersome, with some companies relying on MRI scans of real animals or people [Lan17, SSC*09].…”

Section: Related Workmentioning

confidence: 99%

Muscle and Fascia Simulation with Extended Position Based Dynamics

Romeo¹,

Monteagudo²,

Sánchez-Quirós³

2019

Computer Graphics Forum

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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.

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Physics-aided editing of simulation-ready muscles for visual effects

Cited by 2 publications

References 2 publications

Data‐Driven Facial Simulation

Data‐Driven Facial Simulation

Muscle and Fascia Simulation with Extended Position Based Dynamics

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