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Building anatomically realistic jaw kinematics model from data

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Abstract

Recent work on anatomical face modeling focuses mainly on facial muscles and their activation. This paper considers a different aspect of anatomical face modeling: kinematic modeling of the jaw, i.e., the temporomandibular joint (TMJ). Previous work often relies on simple models of jaw kinematics, even though the actual physiological behavior of the TMJ is quite complex, allowing not only for mouth opening, but also for some amount of sideways (lateral) and front-to-back (protrusion) motions. Fortuitously, the TMJ is the only joint whose kinematics can be accurately measured with optical methods, because the bones of the lower and upper jaw are rigidly connected to the lower and upper teeth. We construct a person-specific jaw kinematic model by asking an actor to exercise the entire range of motion of the jaw while keeping the lips open so that the teeth are at least partially visible. This performance is recorded with three calibrated cameras. We obtain highly accurate 3D models of the teeth with a standard dental scanner and use these models to reconstruct the rigid body trajectories of the teeth from the videos (markerless tracking). The relative rigid transformations samples between the lower and upper teeth are mapped to the Lie algebra of rigid body motions in order to linearize the rotational motion. Our main contribution is to fit these samples with a three-dimensional nonlinear model parameterizing the entire range of motion of the TMJ. We show that standard principal component analysis (PCA) fails to capture the nonlinear trajectories of the moving mandible. However, we found these nonlinearities can be captured with a special modification of autoencoder neural networks known as nonlinear PCA. By mapping back to the Lie group of rigid transformations, we obtain a parametrization of the jaw kinematics which provides an intuitive interface allowing the animators to explore realistic jaw motions in a user-friendly way.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant Numbers IIS-1617172, IIS-1622360 and IIS-1764071. 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. Wenwu Yang was partially funded by the NSF of China (U1609215 and 61472363). Daniel Sýkora was funded by the Fulbright Commission in the Czech Republic, the Technology Agency of the Czech Republic under research program TE01020415 (V3C—Visual Computing Competence Center), and the Grant Agency of the Czech Technical University in Prague (No. SGS17/215/OHK3/3T/18). We also gratefully acknowledge the support of Research Center for Informatics (No. CZ.02.1.01/0.0/0.0/16_019/0000765), Activision, Adobe, and Mitsubishi Electric Research Labs (MERL) as well as hardware donation from NVIDIA Corporation.

Funding

This study was funded by the National Science Foundation (IIS-1617172, IIS-1622360 and IIS-1764071), NSF of China (U1609215 and 61472363), the Fulbright Commission in the Czech Republic, the Technology Agency of the Czech Republic under research program TE01020415 (V3C-Visual Computing Competence Center), the Grant Agency of the Czech Technical University in Prague (No. SGS17/215/OHK3/3T/18), and Research Center for Informatics (No. CZ.02.1.01/0.0/0.0/16_19/0000765).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310012, China

    Wenwu Yang

  2. University of Utah, Salt Lake City, Utah, USA

    Nathan Marshak, Srikumar Ramalingam & Ladislav Kavan

  3. Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Daniel Sýkora

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  1. Wenwu Yang
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Correspondence to Wenwu Yang.

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Conflict of interest

Daniel Sýkora has received research grants from the Fulbright Commission in the Czech Republic. Ladislav Kavan has received a hardware donation from NVIDIA Corporation. Wenwu Yang declares that he has no conflict of interest. Nathan Marshak declares that he has no conflict of interest. Srikumar Ramalingam declares that he has no conflict of interest.

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This work was done when Wenwu Yang was a visiting scholar at the University of Utah.

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Yang, W., Marshak, N., Sýkora, D. et al. Building anatomically realistic jaw kinematics model from data. Vis Comput 35, 1105–1118 (2019). https://doi.org/10.1007/s00371-019-01677-8

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