Estimating the 3-D human body shape and pose under clothing is important for many applications, including virtual try-on, noncontact body measurement, and avatar creation for virtual reality. Existing body shape estimation methods formulate this task as an optimization problem by fitting a parametric body model to a single dressed-human scan or a sequence of dressed-human meshes for a better accuracy. This is impractical for many applications that require fast acquisition, such as gaming and virtual try-on due to the expensive computation. In this article, we propose the first learning-based approach to estimate the human body shape under clothing from a single dressed-human scan, dubbed Body PointNet. The proposed Body PointNet operates directly on raw point clouds and predicts the undressed body in a coarse-to-fine manner. Due to the nature of the data-aligned paired dressed scans and undressed bodies; and genus-0 manifold meshes (i.e., single-layer surfaces)-we face a major challenge of lacking training data. To address this challenge, we propose a novel method to synthesize the dressed-human pseudoscans and corresponding ground truth bodies. A new large-scale dataset, dubbed body under virtual garments, is presented, employed for the learning task of body shape estimation from 3-D dressed-human scans. Comprehensive evaluations show that the proposed Body PointNet outperforms the state-of-the-art methods in terms of both accuracy and running time.
Hu, P, Nourbakhsh Kaashki, N, Dadarlat, V & Munteanu, A 2021, 'Learning to Estimate the Body Shape Under Clothing from a Single 3D Scan', IEEE transactions on industrial informatics, vol. 17, no. 6, 9166710, pp. 3793-3802. https://doi.org/10.1109/TII.2020.3016591
Hu, P., Nourbakhsh Kaashki, N., Dadarlat, V., & Munteanu, A. (2021). Learning to Estimate the Body Shape Under Clothing from a Single 3D Scan. IEEE transactions on industrial informatics, 17(6), 3793-3802. Article 9166710. https://doi.org/10.1109/TII.2020.3016591
@article{05919f0d38c145ddaac5fd30edfa8b20,
title = "Learning to Estimate the Body Shape Under Clothing from a Single 3D Scan",
abstract = "Estimating the 3-D human body shape and pose under clothing is important for many applications, including virtual try-on, noncontact body measurement, and avatar creation for virtual reality. Existing body shape estimation methods formulate this task as an optimization problem by fitting a parametric body model to a single dressed-human scan or a sequence of dressed-human meshes for a better accuracy. This is impractical for many applications that require fast acquisition, such as gaming and virtual try-on due to the expensive computation. In this article, we propose the first learning-based approach to estimate the human body shape under clothing from a single dressed-human scan, dubbed Body PointNet. The proposed Body PointNet operates directly on raw point clouds and predicts the undressed body in a coarse-to-fine manner. Due to the nature of the data-aligned paired dressed scans and undressed bodies; and genus-0 manifold meshes (i.e., single-layer surfaces)-we face a major challenge of lacking training data. To address this challenge, we propose a novel method to synthesize the dressed-human pseudoscans and corresponding ground truth bodies. A new large-scale dataset, dubbed body under virtual garments, is presented, employed for the learning task of body shape estimation from 3-D dressed-human scans. Comprehensive evaluations show that the proposed Body PointNet outperforms the state-of-the-art methods in terms of both accuracy and running time.",
keywords = "Body PointNet, Body shape under clothing",
author = "Pengpeng Hu and {Nourbakhsh Kaashki}, Nastaran and Vasile Dadarlat and Adrian Munteanu",
note = "Funding Information: Manuscript received December 17, 2019; revised July 6, 2020; accepted August 9, 2020. Date of publication August 13, 2020; date of current version March 5, 2021. This work was supported in part by the Innoviris under Project eTailor in close collaboration with Treedy{ extquoteright}s and in part by FWO under Project G084117. Paper no. TII-19-5382. (Corresponding author: Pengpeng Hu.) Pengpeng Hu, Nastaran Nourbakhsh Kaashki, and Adrian Munteanu are with the Department of Electronics and Informatics, Vrije Uni-versiteit Brussel, 1050 Brussels, Belgium (e-mail: phu@etrovub.be; nknourba@etrovub.be; acmuntea@etrovub.be). Publisher Copyright: { extcopyright} 2005-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = jun,
doi = "10.1109/TII.2020.3016591",
language = "English",
volume = "17",
pages = "3793--3802",
journal = "IEEE transactions on industrial informatics",
issn = "1941-0050",
publisher = "IEEE",
number = "6",
}