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Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation

MPS-Authors
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Wang,  Jiayi
Computer Graphics, MPI for Informatics, Max Planck Society;

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Mueller,  Franziska
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons214986

Bernard,  Florian
Computer Graphics, MPI for Informatics, Max Planck Society;

/persons/resource/persons45610

Theobalt,  Christian       
Computer Graphics, MPI for Informatics, Max Planck Society;

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arXiv:2007.03073.pdf
(Preprint), 2MB

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Citation

Wang, J., Mueller, F., Bernard, F., & Theobalt, C. (2020). Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation. Retrieved from https://arxiv.org/abs/2007.03073.


Cite as: https://hdl.handle.net/21.11116/0000-0007-E89A-6
Abstract
We propose to use a model-based generative loss for training hand pose
estimators on depth images based on a volumetric hand model. This additional
loss allows training of a hand pose estimator that accurately infers the entire
set of 21 hand keypoints while only using supervision for 6 easy-to-annotate
keypoints (fingertips and wrist). We show that our partially-supervised method
achieves results that are comparable to those of fully-supervised methods which
enforce articulation consistency. Moreover, for the first time we demonstrate
that such an approach can be used to train on datasets that have erroneous
annotations, i.e. "ground truth" with notable measurement errors, while
obtaining predictions that explain the depth images better than the given
"ground truth".