filename     : Zos19a.pdf
entry        : inproceedings
conference   : SIGGRAPH 2019, Los Angeles, USA, July 28th - August 1st, 2019
pages        : 50:1-50:8
year         : 2019
month        : July
title        : Accurate Markerless Jaw Tracking for Facial Performance Capture
subtitle     :
author       : Gaspard Zoss, Thabo Beeler, Markus Gross, Derek Bradley
booktitle    : ACM Transactions on Graphics (TOG) - SIGGRAPH 2019 Conference Proceedings
ISSN/ISBN    : 0730-0301
editor       : ACM New York, NY, USA 
publisher    : Association for Computing Machinery
publ.place   : New York, NY, USA
volume       : 38
issue        : 4
language     : English
keywords     : Jaw Tracking, Markerless, Data Driven Animation, Facial Performance Capture, Acquisition
abstract     : We present the first method to accurately track the invisible jaw based solely on the visible skin surface, without the need for any markers or augmentation of the actor. As such, the method can readily be integrated with off-the-shelf facial performance capture systems. The core idea is to learn a non-linear mapping from the skin deformation to the underlying jaw motion on a dataset where ground-truth jaw poses have been acquired, and then to retarget the mapping to new subjects. Solving for the jaw pose plays a central role in visual effects pipelines, since accurate jaw motion is required when retargeting to fantasy characters and for physical simulation. Currently, this task is performed mostly manually to achieve the desired level of accuracy, and the presented method has the potential to fully automate this labour intense and error prone process.