Authors:

Yibo JIAO 1, Chang SHU 2, Dinesh K. PAI 1

1 University of British Columbia, Vancouver BC, Canada;
2 National Research Council Canada, Canada

DOI:

https://doi.org/10.15221/24.47

Full paper:

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Keywords:

shape matching, deep learning, anthropometry

Abstract:

Precise localization of anthropometric landmarks is essential in many applications - including computer graphics, computer vision, biomechanics, and morphometric studies. Current methods in machine learning can produce dense correspondence with good global properties, but are poor at localizing specific landmarks. Landmarks are the most important correspondences for many applications and the only independently verifiable criteria. Here we propose a new method for localizing landmarks using learned features that significantly outperforms the state of the art.
Our method learns refined features that characterize the intrinsic and extrinsic geometry around the landmark, thereby making it easy to recognize and localize. We propose a novel loss function for training using two functions to evaluate the likelihood of a vertex being a target landmark: the landmark potential (P), which characterizes the likelihood of a vertex being a landmark and is computed by the network from a given mesh, and the similarity function (D), which measures the distance from a landmark, extrinsically or/and intrinsically, and provides supervision information for training. The network is trained to optimize the correlation between the two functions so that vertices that have high similarity with the target landmark also have high potential, and vice versa. In addition, we introduce nuclear-norm minimization to compute an optimal span of refined features for landmarks among training models. The resulting system is easy to implement and learns efficiently from a small number of meshes with identified landmarks.
Our implantation is available at https://github.com/yibojiao211/Learning_to_Predict_Landmarks.

How to Cite (MLA):

Jiao, Yibo et al., "Learning to Predict Anthropometric Landmarks via Feature Refinement", 3DBODY.TECH Journal, vol. 1, Oct. 2024, #47, https://doi.org/10.15221/24.47.

Presentation:

VIDEO availble in proceedings

Details:

Volume/Issue: 3DBODY.TECH Journal - Vol. 1, 2024
Paper: #47
Published: 2024/10/30
Presented at: 3DBODY.TECH 2024, 22-23 Oct. 2024, Lugano, Switzerland
Proceedings: 3DBODY.TECH 2024 Proceedings

License/Copyright notice

Copyright © 2024 by the author(s).
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The papers appearing in the journal reflect the author's opinions. Their inclusion in the volumes does not necessary constitute endorsement by the editor or by the publisher.

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