Authors:

Joaquin SANCHIZ, Eduardo PARRILLA, Jordi URIEL, Alfredo BALLESTER, Sandra ALEMANY

Instituto de Biomecánica de Valencia IBV, Valencia, Spain

DOI:

https://doi.org/10.15221/24.13

Full paper:

PDF

Keywords:

anthropometry, footwear design, size recommendation, 3D foot, foot reconstruction, real time

Abstract:

Obtaining accurate 3D reconstructions of the human foot from 2D images holds significant importance in various fields, including anthropometry, footwear design, and medical diagnostics. In this study, we propose a novel neural network-based approach for reconstructing a 3D mesh of the foot from a single image. Our method integrates multiple sources of information, including binary segmentation masks and 2D keypoint estimation. By leveraging Principal Component Analysis (PCA) to represent foot morphology in a low-dimensional space, we infer the parameters needed for 3D mesh reconstruction, including rotation and translation parameters for alignment with the input image. Our approach builds upon recent advancements in deep learning for 3D reconstruction from images and demonstrates promising results in accurately capturing foot morphology. The model has been trained with two datasets: one consisting of 1M synthetic samples and another with 500K augmented real samples. Validation on a test subset of over 674 samples resulted in a PA-MPJPE (Procrustes-Aligned Mean Per Joint Position Error) of 0.9 mm. Furthermore, the real-time capability of our method makes it suitable for applications in augmented reality, such as virtual try-on and improvements in user experience and precision of phone-based foot scanning solutions.

How to Cite (MLA):

Sanchiz, Joaquin et al., "Fast and Accurate 3D Foot Reconstruction from a Single Image", 3DBODY.TECH Journal, vol. 1, Oct. 2024, #13, https://doi.org/10.15221/24.13.

Presentation:

VIDEO availble in proceedings

Details:

Volume/Issue: 3DBODY.TECH Journal - Vol. 1, 2024
Paper: #13
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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