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.

@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},
  doi = {10.1109/WACV.2014.6836115},
  month_numeric = {3}
}