Phace: Physics-based Face Modeling and Animation


Alexandru-Eugen Ichim
EPFL
 
Petr Kadlecek
Charles University in Prague
 
Ladislav Kavan
University of Utah
 
Mark Pauly
EPFL
 


Physics-based simulation facilitates a number of advanced effects for facial animation, such as applying wind forces, fattening and slimming of the face, wearing a VR headset, and even turning into a zombie.



Abstract

We present a novel physics-based approach to facial animation. Contrary to commonly used generative methods, our solution computes facial expressions by minimizing a set of non-linear potential energies that model the physical interaction of passive flesh, active muscles, and rigid bone structures. By integrating collision and contact handling into the simulation, our algorithm avoids inconsistent poses commonly observed in generative methods such as blendshape rigs. A novel muscle activation model leads to a robust optimization that faithfully reproduces complex facial articulations. We show how person-specific simulation models can be built from a few expression scans with a minimal data acquisition process and an almost entirely automated processing pipeline. Our method supports temporal dynamics due to inertia or external forces, incorporates skin sliding to avoid unnatural stretching, and offers full control of the simulation parameters, which enables a variety of advanced animation effects. For example, slimming or fattening the face is achieved by simply scaling the volume of the soft tissue elements.We show a series of application demos, including artistic editing of the animation model, simulation of corrective facial surgery, or dynamic interaction with external forces and objects.






Publication

Alexandru-Eugen Ichim, Petr Kadlecek, Ladislav Kavan, Mark Pauly. Phace: Physics-based Face Modeling and Animation. ACM Transactions on Graphics 36(4) [Proceedings of SIGGRAPH], 2017.  


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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant Numbers IIS-1617172 and IIS-1622360, the grant SVV-2017-260452 and GA UK 1524217. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge the support of Activision.