Analyzing Clothing Layer Deformation Statistics of 3D Human Motions

  • Jinlong YangEmail author
  • Jean-Sébastien Franco
  • Franck Hétroy-Wheeler
  • Stefanie Wuhrer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11211)


Recent capture technologies and methods allow not only to retrieve 3D model sequence of moving people in clothing, but also to separate and extract the underlying body geometry, motion component and the clothing as a geometric layer. So far this clothing layer has only been used as raw offsets for individual applications such as retargeting a different body capture sequence with the clothing layer of another sequence, with limited scope, e.g. using identical or similar motions. The structured, semantics and motion-correlated nature of the information contained in this layer has yet to be fully understood and exploited. To this purpose we propose a comprehensive analysis of the statistics of this layer with a simple two-component model, based on PCA subspace reduction of the layer information on one hand, and a generic parameter regression model using neural networks on the other hand, designed to regress from any semantic parameter whose variation is observed in a training set, to the layer parameterization space. We show that this model not only allows to reproduce previous retargeting works, but generalizes the data generation capabilities to other semantic parameters such as clothing variation and size, or physical material parameters with synthetically generated training sequence, paving the way for many kinds of capture data-driven creation and augmentation applications.


Clothing motion analysis 3D garment capture Capture data augmentation and retargeting 



Funded by France National Research grant ANR-14-CE24-0030 ACHMOV. We thank G. Pons-Moll for providing ClothCap [25] data, Kinovis platform( for acquiring the extended Inria dataset, and F. Bertails-Descoubes and A. H. Rasheed for providing the cloth simulator and generating synthetic data.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jinlong Yang
    • 1
    Email author
  • Jean-Sébastien Franco
    • 1
  • Franck Hétroy-Wheeler
    • 2
  • Stefanie Wuhrer
    • 1
  1. 1.Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP(Institute of Engineering Univ. Grenoble Alpes), LJKGrenobleFrance
  2. 2.ICube, University of StrasbourgStrasbourgFrance

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