Efficient Incremental Potential Contact for Actuated Face Simulation

Li, Bo; Yang, Lingchen; Solenthaler, Barbara

doi:10.1145/3610543.3626161

SIGGRAPH Asia 2023 Technical Communications 2023

DOI: 10.1145/3610543.3626161

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Efficient Incremental Potential Contact for Actuated Face Simulation

Bo Li,

Lingchen Yang,

Barbara Solenthaler

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2024

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Cited by 1 publication

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Learning a Generalized Physical Face Model From Data

Yang,

Zoss,

Chandran

et al. 2024

ACM Trans. Graph.

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Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits. Today's methods are data-driven, where the actuations for finite elements are inferred from captured skin geometry. Unfortunately, these approaches have not been widely adopted due to the complexity of initializing the material space and learning the deformation model for each character separately, which often requires a skilled artist followed by lengthy network training. In this work, we aim to make physics-based facial animation more accessible by proposing a generalized physical face model that we learn from a large 3D face dataset. Once trained, our model can be quickly fit to any unseen identity and produce a ready-to-animate physical face model automatically. Fitting is as easy as providing a single 3D face scan, or even a single face image. After fitting, we offer intuitive animation controls, as well as the ability to retarget animations across characters. All the while, the resulting animations allow for physical effects like collision avoidance, gravity, paralysis, bone reshaping and more.

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Learning a Generalized Physical Face Model From Data

Yang,

Zoss,

Chandran

et al. 2024

ACM Trans. Graph.

View full text Add to dashboard Cite

show abstract

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Efficient Incremental Potential Contact for Actuated Face Simulation

Cited by 1 publication

References 9 publications

Learning a Generalized Physical Face Model From Data

Learning a Generalized Physical Face Model From Data

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