Header logo is ps

Model-based Anthropometry: Predicting Measurements from 3D Human Scans in Multiple Poses

2014

Conference Paper

ps


Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custom clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution we call model-based anthropometry. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration with a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-the-art methods.

Author(s): Tsoli, Aggeliki and Loper, Matthew and Black, Michael J
Book Title: Proceedings Winter Conference on Applications of Computer Vision
Pages: 83--90
Year: 2014
Month: March
Publisher: IEEE

Department(s): Perceiving Systems
Research Project(s): Body Applications
Model-based Anthropometry
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: http://dx.doi.org/10.1109/WACV.2014.6836115
Event Name: IEEE Winter Conference on Applications of Computer Vision (WACV)
Event Place: Steamboat Springs, CO, USA

Links: pdf

BibTex

@inproceedings{tsoliWACV14,
  title = {Model-based Anthropometry: Predicting Measurements from 3D Human Scans in Multiple Poses},
  author = {Tsoli, Aggeliki and Loper, Matthew and Black, Michael J},
  booktitle = {Proceedings Winter Conference on Applications of Computer Vision},
  pages = {83--90},
  publisher = {IEEE },
  month = mar,
  year = {2014},
  month_numeric = {3}
}